The impact of rising temperatures on the prevalence of coral diseases and their predictability: a global meta-analysis

This html documents the data calculation, cleaning, modeling, and visualization of a global meta-analysis of coral disease prevalence alongside rising ocean temperatures.

Data Wrangling and Calculation

Load Packages

library(tidyverse)    # ggplot, dplyr, %>%, and friends
library(readxl)       # read in excel files
library(visdat)       # visualize missing data
library(maps)         # map visualization
library(here)         # read in data from project
library(rotl)         # connect with the Open Tree of Life
library(ape)          # for phylogenetic tree manipulation
library(RDS)          # save RDS files
library(BiocManager)  # install and manage Bioconductor packages
library(ggtree)       # devtools::install_github("YuLab-SMU/ggtree")
library(ggtreeExtra)  # devtools::install_github("xiangpin/ggtreeExtra")
library(ggnewscale)   # add colour layers in phylogenetic trees
library(R.utils)      # programming utilities
library(ncdf4)        # read ncdf files
library(lubridate)    # working with dates and times
library(RCurl)        # HTTP interface
library(birk)         # data summaries
library(lme4)         # linear mixed models
library(rstan)        # Stan models
library(glmmTMB)      # run small GLMMs quickly
library(modelr)       # pipelines
library(brms)         # bayesian modeling through Stan
library(tidybayes)    # manipulate Stan objects in a tidy way
library(broom)        # convert model objects to data frames
library(broom.mixed)  # convert brms model objects to data frames
library(emmeans)      # calculate marginal effects in even fancier ways
library(patchwork)    # combine ggplot objects
library(ggokabeito)   # neat accessible color palette
library(gghalves)     # special half geoms
library(ggbeeswarm)   # special distribution-shaped point jittering
library(ggdist)       # distribution visualisation
library(igraph)       # manually alter plots
library(ggpubr)       # manually alter plots
library(ggExtra)      # additional plot tools
library(kableExtra)   # for tables

Load Data

Data was originally organized in an excel file where each sheet of the excel file contained different data with a common identifier between all (Effect Size ID and Paper ID).

Effect Size Data sheet contained information relevant to the effect size calculation (as believed to be relevant at start of project).

Bibliographic Data sheet contained details of the bibliographic information to identify paper and effect size IDs to proper publication.

Transect Data sheet contained information relevant to sample collection (i.e., survey method and dimensions).

Moderator Data sheet contained additional information not directly related to effect size calculation (e.g., Total Sample Area size, number of corals - if provided, etc.).

Disease Data sheet contained all the diseases identified in studies and recorded how many disease incorporated in each disease prevalence metric and which diseases are present (“1”) or absent (“0”) in that sample.

Species Data sheet contained information relevant to the species included in each study. Since most studies do not separate the disease prevalence metric by species, this data sheet was only linked to the others by Paper ID.

# Check that file isn't open in excel
# Read in each excel sheet
prevESD <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Effect Size Data")
bibdata <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Bibliographic Data")
prevTRAN <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Transect Data")
prevMOD <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Moderator Data")
prevDIS <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Disease Data")
prevSPP <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Real Species Data")

# List of included studies
papers <- subset(bibdata, Paper_ID!="")

Organize Data

# visualize missing data
dat.list <- list(prevDIS, prevESD, prevMOD, prevSPP, prevTRAN)

missing<- lapply(dat.list, vis_miss)
missing
# Remove data with no effect sizes
ESD <- subset(prevESD, Effect_ID!="")
MOD <- subset(prevMOD, Effect_ID!="")
TRAN <- subset(prevTRAN, Effect_ID!="")
DIS <- subset(prevDIS, Effect_ID!="")
# Only keep effect sizes measured as disease prevalence in %
percentonly <- subset(prevESD, Unit_Prevalence!="col/100m^2" & 
                        Unit_Prevalence!="col/m^2" & 
                        Unit_Prevalence!="mean no. colonies/m^2" & 
                        Unit_Prevalence!="no. col" & 
                        Effect_ID!="")

# Visualize missing data patterns
vis_miss(percentonly)
# The regions extracted from papers were too precise. Therefore, we redefined regions based on ocean basins
ocean_group <- function(Region_HoeghGuldberg) {
  case_when(
    Region_HoeghGuldberg == "Western Indian Ocean" | Region_HoeghGuldberg=="Eastern Indian Ocean"      ~ "Indian Ocean",
    Region_HoeghGuldberg=="Western Pacific" | Region_HoeghGuldberg=="Coral Triangle & SE Asia" ~ "Pacific Ocean",
    Region_HoeghGuldberg=="Caribbean & Gulf of Mexico" | Region_HoeghGuldberg=="XXXX" ~ "Atlantic Ocean"
  )
}

# Create a new column for oceans
percentonly$Ocean <- ocean_group(percentonly$Region_HoeghGuldberg)

Extract Sea Surface Temperature (SST) Data

As most studies didn’t provide the temperature data at the sample site, we needed to calculate this data using an external dataset. We used NOAA COBE2 available at NOAA Physical Sciences Laboratory website

Load in SST data

# Get data from NOAA database
url = "ftp://ftp.cdc.noaa.gov/Datasets/COBE2/sst.mon.mean.nc"
bin = getBinaryURL(url)
writeBin(bin, "mon.sst.nc")
SSTData <- nc_open(filename = "mon.sst.nc")
rm(bin)
file.remove("mon.sst.nc")

# Set variables from dataset
lon <- ncvar_get(SSTData, varid = "lon")
lat <- ncvar_get(SSTData, varid = "lat")
time <- ncvar_get(SSTData, varid = "time")
sst <- ncvar_get(SSTData, varid = "sst")

# Set time as a date to match extracted data
dim(sst)
SSTData$dim$time$units
sst.date <- as.Date("1891-01-01") + time

Create functions

Needed to extract SST, time, and location

# First define a helper function that extracts middle value (or average of 2 middle values) from a vector).
midval <- function(x) {
  if(length(x)%%2 == 1) {
    return(x[ceiling(length(x)/2)])
  } else return(mean(c(x[length(x)/2], x[length(x)/2 + 1]), na.rm = T))
  
}

# Define a function for extracting mean SST value at a particular location from a particular time period
sst_extract <- function(start_date, end_date, data_source,
                        lon_index, lat_index,
                        summer = F,
                        fun = mean, ...) {
  require("lubridate")
  if(summer) {
    if(lat_index <= 90) {
      month(start_date) <- 6
      end_date <- start_date
      month(end_date) <- 8
    } else if(lat_index > 90) {
      month(start_date) <- 12
      end_date <- start_date
      month(end_date) <- 2
      year(end_date) <- year(end_date) + 1
    }
  }
  
  start_date_units <- as.numeric(start_date - as.Date("1891-01-01"))
  end_date_units <- as.numeric(end_date - as.Date("1891-01-01"))
  start_date_index <- which(time == start_date_units)
  end_date_index <- which(time == end_date_units)
  
  # ...and extract data
  sst_extract <- data_source[lon_index, lat_index, seq(start_date_index, end_date_index)]
  # Here we assign data for i-th record to our list...
  
  return(fun(sst_extract, ...))
}


# Create function which finds the closest coordinate in SST dataset from our extracted dataset
find_close <- function(datasource, lon_ix, lat_ix, step = 3, summer = F, start_date, end_date, diagn = F) {
  lon_steps = seq(lon_ix - step, lon_ix + step)
  lat_steps = seq(lat_ix - step, lat_ix + step)
  
  longitudes = numeric(length(lon_steps)) + 1
  latitudes = numeric(length(lat_steps)) + 1
  names(longitudes) = lon_steps
  names(latitudes) = lat_steps
  
  for(i in 1:length(longitudes)) {
    
    sst_vals = sst_extract(start_date = start_date, end_date = end_date, data_source = datasource,
                           lat_index = lat_ix,
                           lon_index = as.numeric(names(longitudes)[i]),
                           summer = summer,
                           fun = function(x) return(x))
    longitudes[i] = any(!is.na(sst_vals))
    
  }
  
  for(i in 1:length(latitudes)) {
    
    sst_vals = sst_extract(start_date = start_date, end_date = end_date, data_source = datasource,
                           lon_index = lon_ix,
                           lat_index = as.numeric(names(latitudes)[i]),
                           summer = summer,
                           fun = function(x) return(x))
    latitudes[i] = any(!is.na(sst_vals))
    
  }
  
  if (diagn) {
    return(rbind(longitudes, latitudes))
  } else {
    
    if(any(longitudes == 1)) {
      for (k in 1:step) {
        if (longitudes[step + 1 + k] == 1) {
          lon_new = as.numeric(names(longitudes)[step + 1 + k])
          break
        } else if (longitudes[step + 1 - k] == 1) {
          lon_new = as.numeric(names(longitudes)[step + 1 - k])
          break
        }
      }
      lon_ix = lon_new
    } else {
      
      for (k in 1:step) {
        if (latitudes[step + 1 + k] == 1) {
          lat_new = as.numeric(names(latitudes)[step + 1 + k])
          break
        } else if (latitudes[step + 1 - k] == 1) {
          lat_new = as.numeric(names(latitudes)[step + 1 - k])
          break
        }
      }
      lat_ix = lat_new
      
    }
    
    
    return(c(lon_ix, lat_ix))
    
  }
}

# Create a function to identify survey season from sampling period
season_extract <- function(start_date, end_date, lat_index) {
  require('lubridate')
  
  N_seasons <- c(rep('win', 2), rep('spr', 3), rep('sum', 3), rep('aut', 3), 'win')
  S_seasons <- c(rep('sum', 2), rep('aut', 3), rep('win', 3), rep('spr', 3), 'sum')
  
  if (as.numeric(difftime(end_date, start_date)) < 100) {
    
    if (lat_index <= 90) {
      return(names(sort(table(N_seasons[month(start_date):month(end_date)]), decreasing = T))[1])
    } else if (lat_index > 90) {
      return(names(sort(table(S_seasons[month(start_date):month(end_date)]), decreasing = T))[1])
    }
  } else return('multi')
}


# Assign month names to number values
months <- c(Jan = "01", Feb = "02", Mar = "03",
            Apr = "04", May = "05", Jun = "06",
            Jul = "07", Aug = "08", Sep = "09",
            Oct = "10", Nov = "11", Dec = "12")

# Set extracted data as dataframe
percentonly <- as.data.frame(percentonly)

# Create columns for values to go into in dataframe
percentonly$average_SST <- NA
percentonly$middle_SST <- NA
percentonly$sd_SST <- NA
percentonly$start_month <- NA
percentonly$end_month <- NA
percentonly$average_SST_summer <- NA
percentonly$middle_SST_summer <- NA
percentonly$sd_SST_summer <- NA

# Allow coordinates to round to the nearest 0.5 degree to best match to SST dataset
coord_grid <- function(coord) floor(coord) + 0.5

Calculate SST

# Rename in case any variables accidentally get changed, so original is saved
percentonly_ <- percentonly

for (i in 1:nrow(percentonly_)) {
  # cat(i); cat("\n")
  if (grepl("^[A-Z]{1}[a-z]{2}$",
            percentonly_[i, "Month"])) {
    # this condition looks for cases with one month
    
    start.month <- end.month <- percentonly_[i, "Month"]
    start.yr <- end.yr <- percentonly_[i, "Year"]
    
    
  } else if (grepl("^([A-Z]{1}[a-z]{2}), (?:[A-Z]{1}[a-z]{2}, )*([A-Z]{1}[a-z]{2})$",
                   percentonly_[i, "Month"])) {
    # this condition looks for lists of month separated by ", "
    
    start.month <- gsub("^([A-Z]{1}[a-z]{2}), (?:[A-Z]{1}[a-z]{2}, )*([A-Z]{1}[a-z]{2})$",
                        replacement = "\\1",
                        percentonly_[i, "Month"])
    end.month <- gsub("^([A-Z]{1}[a-z]{2}), (?:[A-Z]{1}[a-z]{2}, )*([A-Z]{1}[a-z]{2})$",
                      replacement = "\\2",
                      percentonly_[i, "Month"])
    start.yr <- percentonly_[i, "Year"]
    end.yr <- ifelse(which(names(months) == end.month) > which(names(months) == start.month),
                     start.yr, start.yr + 1)
    
  } else if (grepl("^([A-Z]{1}[a-z]{2})-[A-Z]{1}[a-z]{2}, (?:[A-Z]{1}[a-z]{2}-[A-Z]{1}[a-z]{2}, )*[A-Z]{1}[a-z]{2}-([A-Z]{1}[a-z]{2})$",
                   percentonly_[i, "Month"])) {
    # this condition looks for ranges of months signified by "-" in multiple ranges
    
    start.month <- gsub("^([A-Z]{1}[a-z]{2})-[A-Z]{1}[a-z]{2}, (?:[A-Z]{1}[a-z]{2}-[A-Z]{1}[a-z]{2}, )*[A-Z]{1}[a-z]{2}-([A-Z]{1}[a-z]{2})$",
                        replacement = "\\1",
                        percentonly_[i, "Month"])
    end.month <- gsub("^([A-Z]{1}[a-z]{2})-[A-Z]{1}[a-z]{2}, (?:[A-Z]{1}[a-z]{2}-[A-Z]{1}[a-z]{2}, )*[A-Z]{1}[a-z]{2}-([A-Z]{1}[a-z]{2})$",
                      replacement = "\\2",
                      percentonly_[i, "Month"])
    start.yr <- percentonly_[i, "Year"]
    end.yr <- ifelse(which(names(months) == end.month) > which(names(months) == start.month),
                     start.yr, start.yr + 1)
    
  } else if (grepl("^([A-Z]{1}[a-z]{2})-([A-Z]{1}[a-z]{2})$",
                   percentonly_[i, "Month"])) {
    # this condition looks for ranges of months signified by "-"
    
    start.month <- gsub("^([A-Z]{1}[a-z]{2})-([A-Z]{1}[a-z]{2})$",
                        replacement = "\\1",
                        percentonly_[i, "Month"])
    end.month <- gsub("^([A-Z]{1}[a-z]{2})-([A-Z]{1}[a-z]{2})$",
                      replacement = "\\2",
                      percentonly_[i, "Month"])
    start.yr <- percentonly_[i, "Year"]
    end.yr <- ifelse(which(names(months) == end.month) > which(names(months) == start.month),
                     start.yr, start.yr + 1)
    
  } else if (percentonly_[i, "Month"] == 0) {
    start.yr <- end.yr <- percentonly_[i, "Year"]
    if(percentonly_[i, "Lat"] > 0) start.month <- end.month <- "Jul"
    if(percentonly_[i, "Lat"] < 0) start.month <- end.month <- "Jan"
    
  } else start.month <- end.month <- middle.month <- -999
  
  if (start.month!= -999) {
    lat_index <- which(lat == coord_grid(percentonly_[i, "Lat"])) # ...extract relevant indexes...
    lon_index <- which(lon == coord_grid(ifelse(percentonly_[i, "Lon"] < 0,
                                                360 + percentonly_[i, "Lon"],
                                                percentonly_[i, "Lon"])))
    
    start_date <- as.Date(paste(start.yr, "-", months[start.month], "-01", sep = ""))
    end_date <- as.Date(paste(end.yr, "-", months[end.month], "-01", sep = ""))
    
    
    if(any(is.na(sst_extract(start_date, end_date, sst,
                             lon_index, lat_index, summer = F, function(x) return (x))))) {
      lon_index = find_close(datasource = sst, lon_ix = lon_index, lat_ix = lat_index,
                             step = 10, summer = F, start_date = start_date, end_date = end_date)[1]
      lat_index = find_close(datasource = sst, lon_ix = lon_index, lat_ix = lat_index,
                             step = 10, summer = F, start_date = start_date, end_date = end_date)[2]
    }
    
    percentonly_[i, "start_month"] <- as.numeric(format(start_date, "%m"))
    percentonly_[i, "end_month"] <- as.numeric(format(end_date, "%m"))
    
    percentonly_[i, "average_SST"] <- sst_extract(start_date, end_date, sst,
                                                  lon_index, lat_index, summer = F, mean, na.rm = T)
    percentonly_[i, "middle_SST"] <- sst_extract(start_date, end_date, sst,
                                                 lon_index, lat_index, summer = F, midval)
    percentonly_[i, "sd_SST"] <- sst_extract(start_date, end_date, sst,
                                             lon_index, lat_index, summer = F, sd, na.rm = T)
    percentonly_[i, "average_SST_summer"] <- sst_extract(start_date, end_date, sst,
                                                         lon_index, lat_index, summer = T, mean, na.rm = T)
    percentonly_[i, "middle_SST_summer"] <- sst_extract(start_date, end_date, sst,
                                                        lon_index, lat_index, summer = T, midval)
    percentonly_[i, "sd_SST_summer"] <- sst_extract(start_date, end_date, sst,
                                                    lon_index, lat_index, summer = T, sd, na.rm = T)
    percentonly_[i, "season"] <- season_extract(start_date, end_date, lat_index)
    
    
  } else if (start.month == -999) {
    
  } else stop("Error in month formatting\n")
}



# rename back to original once finished
percentonly <- percentonly_

## Check sst data coverage
out <- array(0, dim = c(360, 180, 2040))
plot(1:360, 1:360, type = "n", ylim = c(1,180))
for (i in 1:360) {
  for (j in 1:180) {
    for(k in 1:2040) {
      if(!is.na(sst[i,j,k])) {
        # points(lon[i], lat[j])
        out[i,j,k] <- out[i,j,k] + 1
      }
    }
  }
}

out1 <- rowSums(out, dims = 2)
dim(out1)
plot(1:360, 1:360, type = "n", ylim = c(180,1))
for (i in 1:360) {
  for(j in 1:180) {
    points(i, j, cex = 0.001 * out1[i,j])
  }
}
# Check if averages make sense with middle month values
ggplot(percentonly, aes(x = average_SST, y = middle_SST)) + geom_point()

# remove NaN results
TStemp <- subset(percentonly, (middle_SST == "NaN"))

Check values

# Check correlation between year and average SST
cor.test(percentonly$Year, percentonly$average_SST)
ggplot(percentonly, mapping = aes(Year, average_SST, col = abs(Lat))) + 
  geom_jitter() +
  geom_smooth()


# Check by lat values without <25
ggplot(percentonly, mapping = aes(Year, middle_SST, col = abs(Lat))) + 
  geom_jitter() +
  geom_smooth()
ggplot(percentonly, mapping = aes(Year, average_SST, col = abs(Lat))) +
  geom_point() +
  geom_smooth(method = "lm")
ggplot(percentonly, mapping = aes(Year, average_SST_summer, col = abs(Lat))) +
  geom_point() +
  geom_smooth(method = "lm")

Combine Data Sheets

# Combine into one dataset
percentonly %>% left_join(MOD, by = c("Effect_ID", "Paper_ID")) %>% 
  left_join(TRAN, by = c("Effect_ID", "Paper_ID")) %>% 
  left_join(DIS, by = c("Effect_ID", "Paper_ID"))      -> dat_with_70s

rdsdat <- dat_with_70s

New SST Database for WSSTA

We needed to utilize a finer resolution database to calculate Weekly Sea Surface Temperature Anomaly (WSSTA), so we chose the daily SST database available through Copernicus which spans from January 1981 to present. This can be accessed at Copernicus Data

We downloaded all between July 1988 and June 2018, unzipped all files into one shared “sst” folder. This folder is separate from the R Project as the R Project was kept in a shared drive and this folder was too big to upload and move (166GB). The files are organized in this folder with the names given by the Copernicus download, which begins with the YearMonthDay of the recorded SST values (e.g., 20180630…).

Load data

Calculation for WSSTA

### load folder with daily sst files
nc_sst_day <- nc_open("D:/Sam's Lenovo/Documents/sst/19880702120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_CDR2.0-v02.0-fv01.0.nc")

#### creation of new NC files with weekly averages ----
# first define dimensions
lonvar <- ncvar_get(nc_sst_day, "lon")
latvar <- ncvar_get(nc_sst_day, "lat")
londim <- ncdim_def("longitude", "degrees_east", lonvar)
latdim <- ncdim_def("latitude", "degrees_north", latvar)
timedim <- ncdim_def("time", "seconds from 1981-1-1", 1, unlim = T)

# now define variables
sstvar <- ncvar_def("sst", "kelvin", dim = list(londim, latdim, timedim), prec = "float")

# create file
sstdata <- nc_create("sst_1988_2018.nc", list(sstvar))
# list individual day files
myfiles <- list.files("D:/Sam's Lenovo/Documents/sst")

# loop over files and fill new NC file
i <- 1 # day counter
j <- 1 # weeks counter
for (current_file in myfiles) {
  
  # open the given sst data in the loop
  nc_temp <- nc_open(paste("D:/Sam's Lenovo/Documents/sst/", current_file, sep = ''))
  sst_temp <- ncvar_get(nc_temp, 'analysed_sst')
  
  if(i == 1) {
    time_temp <- ncvar_get(nc_temp, 'time')
    sst_avg <- (1/7)*sst_temp
  } else {
    sst_avg <- sst_avg + (1/7)*sst_temp
  }
  
  rm(sst_temp)
  nc_close(nc_temp)
  
  if (i == 7) {
    
    # open file to write
    sstdata <- nc_open("sst_1988_2018.nc", write = T)
    
    # write data
    ncvar_put(sstdata, "sst", sst_avg, start = c(1, 1, j), count = c(-1, -1, 1))
    
    # update the time variable with the new day
    ncvar_put(sstdata, "time", time_temp, start = j, count = 1)
    
    # close connection before the next iteration and cleanup data
    nc_close(sstdata)
    rm(sst_avg)
    i <- 1
    j <- j + 1
    
  } else {
    
    i <- i + 1
    
  }
  
  cat("File "); cat(current_file); cat(" done \n")
}


#### extract and add monthly averages maxima ----
mmm <- nc_open(here("data",'ct5km_climatology_v3.1.nc'))
lons <- round(ncvar_get(mmm, 'lon'), 3)
lats <- round(ncvar_get(mmm, 'lat'), 3)

rdsdat$MMM <- NA

# loop over data to fill MMM climatology values
for (i in 1:nrow(rdsdat)) {
  
  if(rdsdat[i, "Year"] > 1989) {
    
    # rounding strategy to get correct rounding resolution
    lon_index <- which(lons == round(floor(rdsdat[i,'Lon']/0.05)*0.05 + 0.025, 3))
    lat_index <- which(lats == round(floor(rdsdat[i,'Lat']/0.05)*0.05 + 0.025, 3))
    
    # adding 273.15 turns Celsius into Kelvin
    mmm_val <- ncvar_get(mmm, 'sst_clim_mmm', 
                         start = c(lon_index, lat_index, 1),
                         count = c(1,1,1)) + 273.15
    
    rdsdat[i, 'MMM'] <- mmm_val
  }
  
  cat('Entry '); cat(i); cat(' done\n')
  
}

nc_close(mmm)


#### extract WSSTA weekly ----
sst <- nc_open('sst_1988_2018.nc')
lons <- round(ncvar_get(sst, 'longitude'), 3)
lats <- round(ncvar_get(sst, 'latitude'), 3)
time <- ncvar_get(sst, 'time')

rdsdat$WSSTA <- NA

for (i in 1:nrow(rdsdat)){
  if (any(is.na(rdsdat[i, "start_month"]))) {
    next
  }
  
  if(rdsdat[i, 'Year'] <= 1989) {
    next
  }
  
  sample.date <- as.Date(paste(rdsdat[i,"Year"], "-",
                               rdsdat[i,"start_month"], "-", "01", sep = ""))
  start.date <- as.numeric(sample.date - as.Date('1981-1-1'))*24*3600
  start.week <- time[which.closest(time, start.date)]
  end.week <- time[which.closest(time, (as.numeric(sample.date - as.Date('1981-1-1'))-365)*24*3600)]
  timewindow <- subset(time, end.week <= time & time <= start.week)
  end.week <- which(time == end.week)
  
  # Get all weekly values from SSTData for given coordinates and input into vector
  lon_index <- which(lons == round(floor(rdsdat[i,'Lon']/0.05)*0.05 + 0.025, 3))
  lat_index <- which(lats == round(floor(rdsdat[i,'Lat']/0.05)*0.05 + 0.025, 3))
  
  
  # Extract variables
  year.sst <- ncvar_get(sst, varid = "sst", start = c(lon_index,lat_index,end.week), count = c(1,1,length(timewindow)))
  # cat(year.sst); cat("\n")
  
  # Check if for every set of coordinates, there is a corresponding temperature
  if (all(is.nan(year.sst) == TRUE)) {
    cat('Entry '); cat(i); cat('needs coordinate adjustments') # Row number that needs coordinate checking for if land
    next
  }
  
  threshold <- rdsdat[i, 'MMM']
  
  # Calculate WSSTA with comparison to threshold
  heatweek <- c()
  for (k in 1:length(year.sst)) {   # k is an index for a position in year.sst
    hotspot.dev <- year.sst[k] - threshold
    if (is.nan(hotspot.dev) == TRUE | is.na(hotspot.dev) == TRUE){
      print("NA")
    }
    if (hotspot.dev > 1) {
      heatweek <- c(heatweek, hotspot.dev)
    } else {
      next   # distinguish between those that were not higher than threshold vs those that were NA or NaN
    }
  }
  rdsdat[i, "WSSTA"] <- sum(heatweek)
  
  cat('Entry '); cat(i); cat(' done\n')
}

nc_close(sst)

Remove Missing Data

# Remove pre-1992 data since WSSTA climatology is from 1985-1992 and don't want to compare values to themselves
dat_post92 <- subset(rdsdat, WSSTA != "Inf")

# Remove NAs
dat_WSSTA <- subset(dat_post92, WSSTA != "")
dat_Area <- subset(dat_WSSTA, Sample_Area_km2 != "")
dat_Tran <- subset(dat_Area, Transect_Type != "")
dat_DisNum <- subset(dat_Tran, Disease_Num != "")

# Rename data to something simpler
rdsdat <- dat_DisNum

Complete Dataset

rdsdat <- readRDS(here("data", "CompletedData.rds"))

kable(rdsdat) %>%  kable_styling("striped", position="left") %>% 
  scroll_box(width = "100%", height = "300px")
ï..Effect_ID Paper_ID Site_ID Disease_Prevalence Unit_Prevalence DiseasePrev_SE SourcePrev Year Month_Old Month SourceYear Location SourceLocation Region_HoeghGuldberg Region_Kleypas Lat_Old Lon_Old Lat Lon SourceLatLon SST_C SourceTemp Notes.x Ocean average_SST middle_SST sd_SST start_month end_month average_SST_summer middle_SST_summer sd_SST_summer season month.Lat season.Lat DiseasePrev_SD SST_SE SST_SD Sample_Area_km2 Site_Num SourceSite_Num Coral_N Mixed_Sp Notes.y Transect_Type Transect_Num Transect_Length_m Transect_Width_m Plot_area_m.2 SourceTransect Notes.x.x Disease_Num WS BBD GA BrB SEB UWS TL DSS WB YBD WPx IMS Trema Cyano PS AN PR PUWS DWS RBD STGA RM RW PLS PWPS CT PBTL WPa Cilia PBSS GPD Unknown Notes.y.y DHW MMM WSSTA
1 EI0001 CD001 SI001 0.7200000 % NA Results p220 1992 Jul Jul Results p220 Key Largo National Marine Sanctuary Materials and Methods p220 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123611 -80.29694 25.123611 -80.29694 Figure 1 Dry Rocks reef, GoogleMaps NA NA 10 corals infected Atlantic Ocean 28.98000 28.9800 NA 7 7 28.67100 28.980 0.8449923 sum 7 summer NA NA NA 9.42400 30 Results p220 1397 1 NA Belt 1 62.8320 2.00 125.6640 Materials and Methods p220 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6960526 302.72 0.000000
2 EI0002 CD001 SI001 0.1400000 % NA Results p220 1992 Nov Nov Results p220 Key Largo National Marine Sanctuary Materials and Methods p220 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123611 -80.29694 25.123611 -80.29694 Figure 1 Dry Rocks reef, GoogleMaps NA NA 2 corals infected Atlantic Ocean 26.46300 26.4630 NA 11 11 28.67100 28.980 0.8449923 aut 11 fall NA NA NA 9.42400 30 Results p220 1397 1 NA Belt 1 62.8320 2.00 125.6640 Materials and Methods p220 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7489243 302.72 0.000000
3 EI0003 CD001 SI001 0.3600000 % NA Results p220 1993 Jul Jul Results p220 Key Largo National Marine Sanctuary Materials and Methods p220 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123611 -80.29694 25.123611 -80.29694 Figure 1 Dry Rocks reef, GoogleMaps NA NA 5 corals infected Atlantic Ocean 29.18300 29.1830 NA 7 7 28.78600 29.183 1.1500923 sum 7 summer NA NA NA 9.42400 30 Results p220 1397 1 NA Belt 1 62.8320 2.00 125.6640 Materials and Methods p220 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4360657 302.72 0.000000
4 EI0004 CD002 SI002 2.9000000 % NA Table 2 2015 0 0 Methods 2015 two-year post-grounding surveys Tubbataha Reefs Natural Park Introduction Coral Triangle & SE Asia Southeast Asia 8.806097 119.81659 8.806097 119.81659 GoogleMaps TRNP South Atoll, Figure 1 NA NA USSG Ground Zero Pacific Ocean 29.20000 29.2000 NA 7 7 29.62267 29.200 0.4632946 sum 7 summer 7.9000000 NA NA 0.38000 9 Methods 2015 two-year post-grounding surveys 285 1 Coral_N USSG Ground Zero and Impact Border combined Belt 9 10.0000 1.00 10.0000 Methods 2015 two-year post-grounding surveys USSG Ground Zero 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2596283 302.69 4.318552
5 EI0005 CD002 SI002 13.5000000 % NA Table 2 2015 0 0 Methods 2015 two-year post-grounding surveys Tubbataha Reefs Natural Park Introduction Coral Triangle & SE Asia Southeast Asia 8.806097 119.81659 8.806097 119.81659 GoogleMaps TRNP South Atoll, Figure 1 NA NA USSG Impact Border Pacific Ocean 29.20000 29.2000 NA 7 7 29.62267 29.200 0.4632946 sum 7 summer 5.4000000 NA NA 0.38000 9 Methods 2015 two-year post-grounding surveys 285 1 Coral_N USSG Ground Zero and Impact Border combined Belt 7 10.0000 1.00 10.0000 Methods 2015 two-year post-grounding surveys USSG Impact Border east: n=4, west: n=3 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2596283 302.69 4.318552
6 EI0006 CD002 SI003 1.6000000 % NA Table 2 2015 0 0 Methods 2015 two-year post-grounding surveys Tubbataha Reefs Natural Park Introduction Coral Triangle & SE Asia Southeast Asia 8.750452 119.82706 8.750452 119.82706 GoogleMaps TRNP South Atoll, Figure 1 NA NA USSG Control (3-S) Pacific Ocean 29.20000 29.2000 NA 7 7 29.62267 29.200 0.4632946 sum 7 summer 2.7000000 NA NA 0.38000 9 Methods 2015 two-year post-grounding surveys 529 1 USSG Control (3-S) Belt 3 20.0000 1.00 20.0000 Methods 2015 two-year post-grounding surveys USSG Control (3-S) 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1853714 302.68 4.252849
7 EI0007 CD002 SI004 8.9000000 % NA Table 2 2015 0 0 Methods 2015 two-year post-grounding surveys Tubbataha Reefs Natural Park Introduction Coral Triangle & SE Asia Southeast Asia 8.849522 119.91465 8.849522 119.91465 GoogleMaps TRNP Ranger Station, Figure 1 NA NA MPY Ground Zero Pacific Ocean 29.20000 29.2000 NA 7 7 29.62267 29.200 0.4632946 sum 7 summer 10.2000000 NA NA 0.38000 9 Methods 2015 two-year post-grounding surveys 274 1 Coral_N MPY Ground Zero and Impact Border combined Belt 3 10.0000 1.00 10.0000 Methods 2015 two-year post-grounding surveys MPY Ground Zero 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2643051 302.68 4.358562
8 EI0008 CD002 SI004 10.4000000 % NA Table 2 2015 0 0 Methods 2015 two-year post-grounding surveys Tubbataha Reefs Natural Park Introduction Coral Triangle & SE Asia Southeast Asia 8.849522 119.91465 8.849522 119.91465 GoogleMaps TRNP Ranger Station, Figure 1 NA NA MPY Impact Border Pacific Ocean 29.20000 29.2000 NA 7 7 29.62267 29.200 0.4632946 sum 7 summer 9.9000000 NA NA 0.38000 9 Methods 2015 two-year post-grounding surveys 274 1 Coral_N MPY Ground Zero and Impact Border combined Belt 9 10.0000 1.00 10.0000 Methods 2015 two-year post-grounding surveys MPY Impact Border lagoonal: n=3, seaweed near: n=3, seaweed far: n=3 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2643051 302.68 4.358562
9 EI0009 CD002 SI004 2.4000000 % NA Table 2 2015 0 0 Methods 2015 two-year post-grounding surveys Tubbataha Reefs Natural Park Introduction Coral Triangle & SE Asia Southeast Asia 8.849522 119.91465 8.849522 119.91465 GoogleMaps TRNP Ranger Station, Figure 1 NA NA MPY Control (1-N) Pacific Ocean 29.20000 29.2000 NA 7 7 29.62267 29.200 0.4632946 sum 7 summer 0.1000000 NA NA 0.38000 9 Methods 2015 two-year post-grounding surveys 1339 1 MPY Control (1-N) Belt 2 20.0000 1.00 20.0000 Methods 2015 two-year post-grounding surveys MPY Control (1-N) 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2643051 302.68 4.358562
10 EI0010 CD003 SI005 0.9028777 % 0.794964 Figure 4, MPA 2013 Aug-Sep Aug-Sep Methods 2.1 p58 Koh Tau Methods 2.1 p58 Coral Triangle & SE Asia Southeast Asia 10.092280 99.83849 10.092280 99.83849 GoogleMaps Koh Tao, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio Pacific Ocean 28.53750 28.5375 0.0601048 8 9 29.07933 29.200 0.5343187 aut 8 summer 1.3769181 NA NA 0.54000 3 Results 3.3.1 p60 6373 1 NA Belt 3 15.0000 2.00 30.0000 Methods 2.1 p58 NA 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Unusual (non-focal) bleaching patterns referred to as Unknown White Syndrome 0.4443054 303.30 0.000000
11 EI0011 CD003 SI005 0.5287770 % 0.3561151 Figure 4, non-MPA 2013 Aug-Sep Aug-Sep Methods 2.1 p58 Koh Tau Methods 2.1 p58 Coral Triangle & SE Asia Southeast Asia 10.092280 99.83849 10.092280 99.83849 GoogleMaps Koh Tao, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio Pacific Ocean 28.53750 28.5375 0.0601048 8 9 29.07933 29.200 0.5343187 aut 8 summer 0.6168095 NA NA 0.54000 3 Results 3.3.1 p60 6373 1 NA Belt 3 15.0000 2.00 30.0000 Methods 2.1 p58 NA 6 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Unusual (non-focal) bleaching patterns referred to as Unknown White Syndrome 0.4443054 303.30 0.000000
12 EI0012 CD004 SI006 4.5000000 % 1.2 Results p4 2011 Dec Dec Methods in situ surveys of belt transects Montebello and Barrow Islands, NW Australia Methods in situ surveys of belt transects Eastern Indian Ocean Australia -20.595150 115.49348 -20.595150 115.49348 GoogleMaps Montebello and Barrow Islands NA NA NA Indian Ocean 27.63000 27.6300 NA 12 12 28.62200 28.938 0.8777522 sum 6 summer NA NA NA 0.52500 12 Figure 1 5498 1 NA Belt 3 15.0000 1.00 15.0000 Methods in situ surveys of belt transects one site had 10m transects 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3735657 302.07 0.000000
13 EI0013 CD006 SI007 0.1650000 % 0.05 Results Spatial and temporal occurrence of disease Coral diseases p457 2010 Jan Jan Materials and Methods Disease surveys p456 Grande Terre, New Caledonia Figure 1 Western Pacific Melanesia -21.432377 165.45553 -21.459280 165.12087 GoogleMaps Grande Terre, Figure 1 NA NA NA Pacific Ocean 26.64000 26.6400 NA 1 1 27.15633 27.548 0.7576666 sum 7 summer NA NA NA 3.78000 13 Table 1 47166 1 NA Belt 2 25.0000 1.00 25.0000 Materials and Methods Disease Surveys p456 NA 11 1 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 disease reported as genus-specific, but here grouped based on visual symptom 1.5699768 300.27 5.188527
14 EI0014 CD006 SI007 0.2870000 % 0.1 Results Spatial and temporal occurrence of disease Coral diseases p457 2013 Feb Feb Materials and Methods Disease surveys p456 Grande Terre, New Caledonia Figure 1 Western Pacific Melanesia -21.432377 165.45553 -21.459280 165.12087 GoogleMaps Grande Terre, Figure 1 NA NA NA Pacific Ocean 27.40800 27.4080 NA 2 2 26.83100 27.048 0.5318114 sum 8 summer NA NA NA 3.60000 12 Table 1 38251 1 NA Belt 2 25.0000 1.00 25.0000 Materials and Methods Disease Surveys p456 NA 9 1 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 disease reported as genus-specific, but here grouped based on visual symptom 1.0121460 300.27 4.537109
15 EI0015 CD008 SI008 4.9000000 % NA Table 2A 1998 Aug Aug Table 1A Andros Island, Bahamas Methods p78 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.355060 -77.66884 24.355060 -77.66884 Averaged from Table 1A NA NA Reef-crest sites Atlantic Ocean 30.02500 30.0250 NA 8 8 29.48933 29.753 0.7054766 sum 8 summer 3.2000000 NA NA 0.70000 7 Table 2 744 1 NA Line 10 10.0000 1.00 10.0000 Methods p79, Table 2 NA 2 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8371277 302.26 0.000000
17 EI0017 CD007 SI010 0.0021000 % NA Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8021622 301.60 15.488447
18 EI0018 CD007 SI010 0.0007128 % NA Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8021622 301.60 15.488447
19 EI0019 CD007 SI010 0.0000000 % 0 Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8021622 301.60 15.488447
20 EI0020 CD007 SI010 0.0014000 % NA Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8021622 301.60 15.488447
21 EI0021 CD007 SI010 0.0043000 % NA Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8021622 301.60 15.488447
22 EI0022 CD007 SI010 0.0000000 % 0 Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8021622 301.60 15.488447
23 EI0023 CD007 SI010 0.0007128 % NA Table 1 calculated using sample size in Results p4 2005 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.75000 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14400 29.1440 0.4115367 10 11 29.18767 29.065 0.2910778 aut 10 fall NA NA NA 1.29000 16 Results p4 1403 1 NA Line 129 10.0000 1.00 10.0000 Results p4, Methods p3 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 NA 0.8021622 301.60 15.488447
24 EI0024 CD007 SI010 0.0028000 % NA Table 1 calculated using sample size in Results p5 2006 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.12500 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14000 29.1400 0.2899137 10 11 28.67600 28.525 0.2093630 aut 10 fall NA NA NA 1.32000 16 Results p3 1416 1 NA Line 132 10.0000 1.00 10.0000 Results p3, Methods p3 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5164490 301.60 9.275682
25 EI0025 CD007 SI010 0.0007062 % NA Table 1 calculated using sample size in Results p5 2006 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.12500 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14000 29.1400 0.2899137 10 11 28.67600 28.525 0.2093630 aut 10 fall NA NA NA 1.32000 16 Results p3 1416 1 NA Line 132 10.0000 1.00 10.0000 Results p3, Methods p3 NA 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5164490 301.60 9.275682
26 EI0026 CD007 SI010 0.0042000 % NA Table 1 calculated using sample size in Results p5 2006 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.12500 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14000 29.1400 0.2899137 10 11 28.67600 28.525 0.2093630 aut 10 fall NA NA NA 1.32000 16 Results p3 1416 1 NA Line 132 10.0000 1.00 10.0000 Results p3, Methods p3 NA 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5164490 301.60 9.275682
27 EI0027 CD007 SI010 0.0106000 % NA Table 1 calculated using sample size in Results p5 2006 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.12500 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14000 29.1400 0.2899137 10 11 28.67600 28.525 0.2093630 aut 10 fall NA NA NA 1.32000 16 Results p3 1416 1 NA Line 132 10.0000 1.00 10.0000 Results p3, Methods p3 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5164490 301.60 9.275682
28 EI0028 CD007 SI010 0.0141000 % NA Table 1 calculated using sample size in Results p5 2006 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.12500 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14000 29.1400 0.2899137 10 11 28.67600 28.525 0.2093630 aut 10 fall NA NA NA 1.32000 16 Results p3 1416 1 NA Line 132 10.0000 1.00 10.0000 Results p3, Methods p3 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5164490 301.60 9.275682
29 EI0029 CD007 SI010 0.0014000 % NA Table 1 calculated using sample size in Results p5 2006 Oct-Nov Oct-Nov Materials and Methods p3 Dominica, West Indies Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 15.380620 -61.41153 15.380620 -61.41153 Figure 1, site 8, converted to decimal using SB excel calculator 29.12500 Averaged from Results Sea Temperature p6 NA Atlantic Ocean 29.14000 29.1400 0.2899137 10 11 28.67600 28.525 0.2093630 aut 10 fall NA NA NA 1.32000 16 Results p3 1416 1 NA Line 132 10.0000 1.00 10.0000 Results p3, Methods p3 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5164490 301.60 9.275682
30 EI0030 CD010 SI011 22.0833300 % 1.66667 Figure 6b, Metadigitise 2004 0 0 Figure 6b Upper Florida Keys, USA Materials and Methods Survey methods p370 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123610 -80.29736 25.123610 -80.29736 Table 1, Key Largo Dry Rocks, converted using SB excel calculator NA NA NA Atlantic Ocean 29.47300 29.4730 NA 7 7 29.14867 29.473 0.7002977 sum 7 summer 6.2360960 NA NA 0.15000 5 Materials and Methods Survey methods p370 556 0 NA Circle 15 NA NA 10.0000 Materials and Methods Survey methods p370-372, plots are circular NA 3 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.2314301 302.72 0.000000
31 EI0031 CD010 SI011 30.6250000 % 1.875 Figure 6b, Metadigitise 2005 0 0 Figure 6b Upper Florida Keys, USA Materials and Methods Survey methods p370 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123610 -80.29736 25.123610 -80.29736 Table 1, Key Largo Dry Rocks, converted using SB excel calculator NA NA NA Atlantic Ocean 28.97300 28.9730 NA 7 7 28.88633 28.973 1.1973554 sum 7 summer 7.0156080 NA NA 0.15000 5 Materials and Methods Survey methods p370 468 0 NA Circle 15 NA NA 10.0000 Materials and Methods Survey methods p370-372, plots are circular NA 3 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 1.2357025 302.72 1.048555
32 EI0032 CD010 SI011 10.6250000 % 1.25 Figure 6b, Metadigitise 2006 0 0 Figure 6b Upper Florida Keys, USA Materials and Methods Survey methods p370 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123610 -80.29736 25.123610 -80.29736 Table 1, Key Largo Dry Rocks, converted using SB excel calculator NA NA NA Atlantic Ocean 28.92800 28.9280 NA 7 7 28.82200 28.928 0.8609082 sum 7 summer 4.6770720 NA NA 0.15000 5 Materials and Methods Survey methods p370 373 0 NA Circle 15 NA NA 10.0000 Materials and Methods Survey methods p370-372, plots are circular NA 3 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.3421478 302.72 3.682837
33 EI0033 CD010 SI011 11.8750000 % 1.66667 Figure 6b, Metadigitise 2007 0 0 Figure 6b Upper Florida Keys, USA Materials and Methods Survey methods p370 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123610 -80.29736 25.123610 -80.29736 Table 1, Key Largo Dry Rocks, converted using SB excel calculator NA NA NA Atlantic Ocean 29.48000 29.4800 NA 7 7 29.04033 29.480 1.1145572 sum 7 summer 6.2360960 NA NA 0.15000 5 Materials and Methods Survey methods p370 337 0 NA Circle 15 NA NA 10.0000 Materials and Methods Survey methods p370-372, plots are circular NA 3 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 1.0682373 302.72 0.000000
34 EI0034 CD010 SI011 17.5000000 % 2.08333 Figure 6b, Metadigitise 2008 0 0 Figure 6b Upper Florida Keys, USA Materials and Methods Survey methods p370 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123610 -80.29736 25.123610 -80.29736 Table 1, Key Largo Dry Rocks, converted using SB excel calculator NA NA NA Atlantic Ocean 28.87800 28.8780 NA 7 7 28.79367 28.878 0.8247400 sum 7 summer 7.7951200 NA NA 0.15000 5 Materials and Methods Survey methods p370 318 0 NA Circle 15 NA NA 10.0000 Materials and Methods Survey methods p370-372, plots are circular NA 3 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.7328186 302.72 4.457100
35 EI0035 CD010 SI011 17.5000000 % 2.08333 Figure 6b, Metadigitise 2009 0 0 Figure 6b Upper Florida Keys, USA Materials and Methods Survey methods p370 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.123610 -80.29736 25.123610 -80.29736 Table 1, Key Largo Dry Rocks, converted using SB excel calculator NA NA NA Atlantic Ocean 29.38500 29.3850 NA 7 7 29.08367 29.385 0.9514864 sum 7 summer 7.7951200 NA NA 0.15000 5 Materials and Methods Survey methods p370 298 0 NA Circle 15 NA NA 10.0000 Materials and Methods Survey methods p370-372, plots are circular NA 3 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.2935791 302.72 0.000000
36 EI0036 CD044 SI012 3.5600000 % NA p1407 2014 Sep Sep p1407 Vaan Island, Gulf of Mannar p1407 Eastern Indian Ocean Central Indian 8.833330 78.21667 8.833330 78.21667 p1407, GoogleMaps NA NA NA Indian Ocean 27.95000 27.9500 NA 9 9 28.11700 27.950 0.4893596 aut 9 fall 1.2600000 NA NA 0.24000 1 p1407 NA 0 NA Line 12 20.0000 1.00 20.0000 p1407 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4628754 303.61 0.000000
39 EI0042 CD014 SI015 71.9424460 % 28.41726619 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Sumilon Island East Figure 5 Coral Triangle & SE Asia Southeast Asia 9.295181 123.38678 9.295181 123.38678 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 49.2201489 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5528564 302.19 0.000000
40 EI0043 CD014 SI015 55.0359712 % 7.73381295 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Sumilon Island West Figure 5 Coral Triangle & SE Asia Southeast Asia 9.295181 123.38678 9.295181 123.38678 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 13.3953570 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5528564 302.19 0.000000
41 EI0044 CD014 SI016 44.9640288 % 4.67625899 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 North Bais Bay Figure 5 Coral Triangle & SE Asia Southeast Asia 9.636538 123.22198 9.636538 123.22198 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 8.0995182 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5439224 302.17 7.349969
42 EI0045 CD014 SI016 20.6834532 % 8.63309353 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Bato, Cebu Figure 5 Coral Triangle & SE Asia Southeast Asia 9.636538 123.22198 9.636538 123.22198 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 14.9529566 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5439224 302.17 7.349969
43 EI0046 CD014 SI015 14.0287770 % 9.71223022 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Cogon Reef and Marine Reserve Figure 5 Coral Triangle & SE Asia Southeast Asia 9.295181 123.38678 9.295181 123.38678 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 16.8220762 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5528564 302.19 0.000000
44 EI0047 CD014 SI016 10.9712230 % 1.43884892 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Bolisong, Negros Oriental Figure 5 Coral Triangle & SE Asia Southeast Asia 9.636538 123.22198 9.636538 123.22198 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 2.4921594 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5439224 302.17 7.349969
45 EI0048 CD014 SI016 10.4316547 % 7.1942446 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 South Bais Bay Figure 5 Coral Triangle & SE Asia Southeast Asia 9.636538 123.22198 9.636538 123.22198 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 12.4607972 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5439224 302.17 7.349969
46 EI0049 CD014 SI015 8.2733813 % 11.51079137 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Cangalwang, Siquijor Island Figure 5 Coral Triangle & SE Asia Southeast Asia 9.295181 123.38678 9.295181 123.38678 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 19.9372755 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5528564 302.19 0.000000
47 EI0050 CD014 SI015 0.0000000 % 0 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Apo Island Figure 5 Coral Triangle & SE Asia Southeast Asia 9.295181 123.38678 9.295181 123.38678 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 0.0000000 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5528564 302.19 0.000000
48 EI0051 CD014 SI015 0.0000000 % 0 Figure 5 1998 Feb, Apr, Jun, Aug, Oct, Dec Feb, Apr, Jun, Aug, Oct, Dec Materials and Methods p97 Bantayan, Negros Oriental Figure 5 Coral Triangle & SE Asia Southeast Asia 9.295181 123.38678 9.295181 123.38678 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, Location from centralized location Pacific Ocean 29.29382 30.2280 1.0311239 2 12 30.21200 30.228 0.0920494 multi 2 winter 0.0000000 NA NA 0.75000 1 Figure 5 NA 1 1 site stated because the effect sizes are split by site for this paper Belt 3 25.0000 1.00 25.0000 Materials and Methods p97 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5528564 302.19 0.000000
49 EI0052 CD017 SI017 14.2000000 % NA Table 1 2005 May May Methods 2.2 Disease prevalence and incidence p2 Northwestern Hawaiian Islands Methods 2.1 Description of sites p2 Western Pacific Polynesia 23.833333 -166.16667 23.833333 -166.16667 Methods 2.1 Description of sites p2, GoogleMaps NA NA NA Pacific Ocean 25.18800 25.1880 NA 5 5 26.76333 26.670 0.4080845 spr 5 spring NA NA NA 1.20000 1 Methods 2.1 Description of sites p2 183 0 Only one species identified at site Belt 2 25.0000 6.00 150.0000 Methods 2.2 Disease prevalence and incidence p2, ran out of time, so estimated prevalence based on 25 x 2m transect NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2250061 300.18 0.000000
50 EI0053 CD017 SI017 8.7430000 % NA Table 1 2006 May May Methods 2.2 Disease prevalence and incidence p2 Northwestern Hawaiian Islands Methods 2.1 Description of sites p2 Western Pacific Polynesia 23.833333 -166.16667 23.833333 -166.16667 Methods 2.1 Description of sites p2, GoogleMaps NA NA NA Pacific Ocean 24.59500 24.5950 NA 5 5 26.50700 26.500 0.4425416 spr 5 spring NA NA NA 1.20000 1 Methods 2.1 Description of sites p2 183 0 Only one species identified at site Belt 2 25.0000 6.00 150.0000 Methods 2.2 Disease prevalence and incidence p2, ran out of time, so estimated prevalence based on 25 x 2m transect NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2428436 300.18 0.000000
51 EI0054 CD017 SI018 4.2000000 % NA Table 1 2008 Jun Jun Methods 2.2 Disease prevalence and incidence p2 American Samoa Methods 2.1 Description of sites p2 Western Pacific Polynesia -14.233333 -170.66667 -14.233333 -170.66667 Methods 2.1 Description of sites p2, GoogleMaps NA NA NA Pacific Ocean 28.34500 28.3450 NA 6 6 29.18767 29.258 0.0804130 win 12 winter NA NA NA 1.20000 1 Methods 2.1 Description of sites p2 309 1 NA Belt 2 25.0000 6.00 150.0000 Methods 2.2 Disease prevalence and incidence p2, ran out of time, so estimated prevalence based on 25 x 2m transect NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3707581 302.08 0.000000
52 EI0055 CD017 SI018 1.2940000 % NA Table 1 2009 Sep Sep Methods 2.2 Disease prevalence and incidence p2 American Samoa Methods 2.1 Description of sites p2 Western Pacific Polynesia -14.233333 -170.66667 -14.233333 -170.66667 Methods 2.1 Description of sites p2, GoogleMaps NA NA NA Pacific Ocean 27.66000 27.6600 NA 9 9 29.06534 29.453 0.3422513 spr 3 spring NA NA NA 1.20000 1 Methods 2.1 Description of sites p2 309 1 NA Belt 2 25.0000 6.00 150.0000 Methods 2.2 Disease prevalence and incidence p2, ran out of time, so estimated prevalence based on 25 x 2m transect NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3800049 302.08 2.175695
56 EI0059 CD019 SI022 0.2700000 % 0.08 Results Distribution, prevalence, and seasonality of MWS p4 2006 Sep Sep Materials and Methods Distribution, prevalence, and seasonality of MWS p2 Kaneohe Bay, Oahu, HI Materials and Methods Study site p2 Western Pacific Polynesia 21.464469 -157.81539 21.464469 -157.81539 GoogleMaps NA NA NA Pacific Ocean 26.71300 26.7130 NA 9 9 25.83500 25.880 0.4890554 aut 9 fall NA NA NA 1.35000 9 Materials and Methods Distribution, prevalence, and seasonality of MWS p2 NA 0 NA Belt 2 25.0000 6.00 150.0000 Materials and Methods Distribution, prevalence, and seasonality of MWS p2 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.0814362 300.02 0.000000
57 EI0060 CD019 SI022 0.3500000 % 0.13 Results Distribution, prevalence, and seasonality of MWS p4 2007 May May Materials and Methods Distribution, prevalence, and seasonality of MWS p2 Kaneohe Bay, Oahu, HI Materials and Methods Study site p2 Western Pacific Polynesia 21.464469 -157.81539 21.464469 -157.81539 GoogleMaps NA NA NA Pacific Ocean 25.00000 25.0000 NA 5 5 25.96267 25.940 0.3295856 spr 5 spring NA NA NA 1.35000 9 Materials and Methods Distribution, prevalence, and seasonality of MWS p2 NA 0 NA Belt 2 25.0000 6.00 150.0000 Materials and Methods Distribution, prevalence, and seasonality of MWS p2 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6685486 300.02 0.000000
58 EI0061 CD022 SI023 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Carmelitas North, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
59 EI0062 CD022 SI023 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Carmelitas South, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
60 EI0063 CD022 SI023 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mujeres West, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
61 EI0064 CD022 SI023 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mujeres East, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
62 EI0065 CD022 SI023 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Carbinero, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
63 EI0066 CD022 SI023 1.8987342 % NA Figure 3 1996 0 0 Figure 3 Carmelitas North, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
64 EI0067 CD022 SI023 2.6582278 % NA Figure 3 1996 0 0 Figure 3 Carmelitas South, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
65 EI0068 CD022 SI023 0.0000000 % NA Figure 3 1996 0 0 Figure 3 Mujeres West, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
66 EI0069 CD022 SI023 0.0000000 % NA Figure 3 1996 0 0 Figure 3 Mujeres East, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
67 EI0070 CD022 SI023 0.0000000 % NA Figure 3 1996 0 0 Figure 3 Carbinero, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
68 EI0071 CD022 SI023 15.0632911 % NA Figure 3 1999 0 0 Figure 3 Carmelitas North, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
69 EI0072 CD022 SI023 49.6202532 % NA Figure 3 1999 0 0 Figure 3 Carmelitas South, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
70 EI0073 CD022 SI023 19.1139241 % NA Figure 3 1999 0 0 Figure 3 Mujeres West, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
71 EI0074 CD022 SI023 6.2025316 % NA Figure 3 1999 0 0 Figure 3 Mujeres East, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
72 EI0075 CD022 SI023 1.3924051 % NA Figure 3 1999 0 0 Figure 3 Carbinero, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
73 EI0076 CD022 SI023 26.0759494 % NA Figure 3 2000 0 0 Figure 3 Carmelitas North, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
74 EI0077 CD022 SI023 58.4810127 % NA Figure 3 2000 0 0 Figure 3 Carmelitas South, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
75 EI0078 CD022 SI023 18.1012658 % NA Figure 3 2000 0 0 Figure 3 Mujeres West, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
76 EI0079 CD022 SI023 9.3670886 % NA Figure 3 2000 0 0 Figure 3 Mujeres East, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
77 EI0080 CD022 SI023 2.1518987 % NA Figure 3 2000 0 0 Figure 3 Carbinero, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
78 EI0081 CD022 SI023 24.8101266 % NA Figure 3 2001 0 0 Figure 3 Carmelitas North, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
79 EI0082 CD022 SI023 54.5569620 % NA Figure 3 2001 0 0 Figure 3 Carmelitas South, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
80 EI0083 CD022 SI023 18.3544304 % NA Figure 3 2001 0 0 Figure 3 Mujeres West, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
81 EI0084 CD022 SI023 10.8860759 % NA Figure 3 2001 0 0 Figure 3 Mujeres East, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
82 EI0085 CD022 SI023 3.5443038 % NA Figure 3 2001 0 0 Figure 3 Carbinero, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
83 EI0086 CD022 SI023 15.4430380 % NA Figure 3 2003 0 0 Figure 3 Carmelitas North, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
84 EI0087 CD022 SI023 39.3670886 % NA Figure 3 2003 0 0 Figure 3 Carmelitas South, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
85 EI0088 CD022 SI023 13.9240506 % NA Figure 3 2003 0 0 Figure 3 Mujeres West, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
86 EI0089 CD022 SI023 15.8227848 % NA Figure 3 2003 0 0 Figure 3 Mujeres East, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
87 EI0090 CD022 SI023 3.6708861 % NA Figure 3 2003 0 0 Figure 3 Carbinero, Mona Island, Puerto Rico Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.078674 -67.89264 18.078674 -67.89264 GoogleMaps for Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 2 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 3.14000 5 Materials and Methods p68 2166 1 Data aggregated for whole study Circle 2 20.0000 NA 314.0000 Materials and Methods p68 length = diameter, more a “site” than a transect 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
88 EI0091 CD026 SI024 0.3800000 % 0.1 Results p1039 2007 Nov Nov Results p1039 Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA GA Pacific Ocean 25.44300 25.4430 NA 11 11 27.12033 27.273 0.2044906 spr 5 spring NA NA NA 0.64500 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 7 15.0000 1.00 15.0000 Methods Disease surveys p1037, only 43 transects conducted this year NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6199951 300.27 0.000000
89 EI0092 CD026 SI024 0.1200000 % 0.05 Figure 3b 2007 Nov Nov Figure 3b Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA BrB Prevalence extracted using MetaDigitise in Rstudio, sample size 43 Pacific Ocean 25.44300 25.4430 NA 11 11 27.12033 27.273 0.2044906 spr 5 spring 0.3278719 NA NA 0.64500 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 7 15.0000 1.00 15.0000 Methods Disease surveys p1037, only 43 transects conducted this year NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6199951 300.27 0.000000
90 EI0093 CD026 SI024 1.1200000 % 0.31 Results p1038 2007 Nov Nov Results p1038 Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA UWS Pacific Ocean 25.44300 25.4430 NA 11 11 27.12033 27.273 0.2044906 spr 5 spring NA NA NA 0.64500 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 7 15.0000 1.00 15.0000 Methods Disease surveys p1037, only 43 transects conducted this year NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6199951 300.27 0.000000
91 EI0094 CD026 SI024 0.1300000 % 0.08 Figure 3b 2008 Jan Jan Figure 3b Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA BrB Prevalence extracted using MetaDigitise in Rstudio, sample size 48 Pacific Ocean 27.27300 27.2730 NA 1 1 27.26767 27.258 0.1377544 sum 7 summer 0.5542563 NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6199951 300.27 0.000000
92 EI0095 CD026 SI024 2.6700000 % 0.52 Results p1038 2008 Jan Jan Results p1038 Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA UWS Pacific Ocean 27.27300 27.2730 NA 1 1 27.26767 27.258 0.1377544 sum 7 summer NA NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6199951 300.27 0.000000
93 EI0096 CD026 SI024 3.2900000 % 0.58 Results p1038 2008 Aug Aug Results p1038 Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA BrB Pacific Ocean 21.73800 21.7380 NA 8 8 27.26767 27.258 0.1377544 win 2 winter NA NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2974930 300.27 0.000000
94 EI0097 CD026 SI024 0.0900000 % 0.05 Figure 3b 2008 Aug Aug Figure 3b Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA UWS Prevalence extracted using MetaDigitise in Rstudio, sample size 48 Pacific Ocean 21.73800 21.7380 NA 8 8 27.26767 27.258 0.1377544 win 2 winter 0.3464102 NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2974930 300.27 0.000000
95 EI0098 CD026 SI024 0.1400000 % 0.11 Figure 3b 2009 Jan Jan Figure 3b Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA BrB Prevalence extracted using MetaDigitise in Rstudio, sample size 48 Pacific Ocean 27.25800 27.2580 NA 1 1 27.63767 27.765 0.4255358 sum 7 summer 0.7621024 NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3821106 300.27 0.000000
96 EI0099 CD026 SI024 0.8600000 % 0.37 Figure 3b 2009 Jan Jan Figure 3b Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA UWS Prevalence extracted using MetaDigitise in Rstudio, sample size 48 Pacific Ocean 27.25800 27.2580 NA 1 1 27.63767 27.765 0.4255358 sum 7 summer 2.5634352 NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3821106 300.27 0.000000
97 EI0100 CD026 SI024 0.8200000 % 0.14 Results p1039 2009 Aug Aug Results p1039 Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA GA Pacific Ocean 22.01800 22.0180 NA 8 8 27.63767 27.765 0.4255358 win 2 winter NA NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6585693 300.27 0.000000
98 EI0101 CD026 SI024 1.5300000 % 0.28 Results p1038 2009 Aug Aug Results p1038 Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA BrB Pacific Ocean 22.01800 22.0180 NA 8 8 27.63767 27.765 0.4255358 win 2 winter NA NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6585693 300.27 0.000000
99 EI0102 CD026 SI024 0.3200000 % 0.14 Figure 3b 2009 Aug Aug Figure 3b Heron Island Figure 1 Western Pacific Australia -23.449452 151.97768 -23.449452 151.97768 GoogleMaps for Heron Reef NA NA UWS Prevalence extracted using MetaDigitise in Rstudio, sample size 48 Pacific Ocean 22.01800 22.0180 NA 8 8 27.63767 27.765 0.4255358 win 2 winter 0.9699484 NA NA 0.72000 6 Figure 1 36030 1 CoralNum aggregated for whole study, other metrics effect size specific Belt 8 15.0000 1.00 15.0000 Methods Disease surveys p1037 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6585693 300.27 0.000000
100 EI0103 CD027 SI025 1.4545455 % 0 Figure 3 2008 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 27.95050 27.9505 0.7813540 2 3 28.25700 28.293 0.0821455 aut 8 summer 0.0000000 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6814270 301.97 0.000000
101 EI0104 CD027 SI026 0.7272727 % 0 Figure 3 2008 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 27.95050 27.9505 0.7813540 2 3 28.25700 28.293 0.0821455 aut 8 summer 0.0000000 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6814270 301.97 0.000000
102 EI0105 CD027 SI025 4.3636364 % 1.45454545 Figure 3 2008 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 23.17800 23.1780 NA 6 6 28.25700 28.293 0.0821455 win 12 winter 3.2524625 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9414368 301.97 0.000000
103 EI0106 CD027 SI026 1.4545455 % 0.72727273 Figure 3 2008 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 23.17800 23.1780 NA 6 6 28.25700 28.293 0.0821455 win 12 winter 1.6262313 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9414368 301.97 0.000000
104 EI0107 CD027 SI025 63.8000000 % 3.03 Results Disease dynamics and rainfall p819 2009 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 27.89800 27.8980 0.3747671 2 3 28.37600 28.318 0.3882630 aut 8 summer NA NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4192963 301.97 1.084291
105 EI0108 CD027 SI026 60.6000000 % 3.8 Results Disease dynamics and rainfall p819 2009 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 27.89800 27.8980 0.3747671 2 3 28.37600 28.318 0.3882630 aut 8 summer NA NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4192963 301.97 1.084291
106 EI0109 CD027 SI025 0.7272727 % 0.36363636 Figure 3 2009 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 23.68300 23.6830 NA 6 6 28.37600 28.318 0.3882630 win 12 winter 0.8131156 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4064484 301.97 2.084293
107 EI1020 CD027 SI025 7.2727273 % 2.18181818 Figure 3 2010 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 28.55750 28.5575 0.3288048 2 3 28.16433 28.245 0.0764288 aut 8 summer 4.8786938 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7428589 301.97 1.000002
108 EI1021 CD027 SI025 0.0000000 % 0 Figure 3 2010 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 23.78500 23.7850 NA 6 6 28.16433 28.245 0.0764288 win 12 winter 0.0000000 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.0157242 301.97 1.692842
109 EI1022 CD027 SI025 1.4545455 % 4.72727273 Figure 3 2011 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 28.07000 28.0700 0.1202082 2 3 28.25267 28.085 0.4288319 aut 8 summer 10.5705032 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6656952 301.97 3.241398
110 EI1023 CD027 SI025 0.0000000 % 0 Figure 3 2011 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Nelly Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.163570 146.85079 -19.163570 146.85079 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 22.92500 22.9250 NA 6 6 28.25267 28.085 0.4288319 win 12 winter 0.0000000 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7796326 301.97 1.548555
111 EI0110 CD027 SI026 1.4545455 % 0.72727273 Figure 3 2009 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 23.68300 23.6830 NA 6 6 28.37600 28.318 0.3882630 win 12 winter 1.6262313 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4064484 301.97 2.084293
112 EI1024 CD027 SI026 15.6363636 % 3.27272727 Figure 3 2010 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 28.55750 28.5575 0.3288048 2 3 28.16433 28.245 0.0764288 aut 8 summer 7.3180407 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7428589 301.97 1.000002
113 EI1025 CD027 SI026 0.0000000 % 0 Figure 3 2010 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 23.78500 23.7850 NA 6 6 28.16433 28.245 0.0764288 win 12 winter 0.0000000 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.0157242 301.97 1.692842
114 EI1026 CD027 SI026 23.6363636 % 0.36363636 Figure 3 2011 Feb-Mar Feb-Mar Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 28.07000 28.0700 0.1202082 2 3 28.25267 28.085 0.4288319 aut 8 summer 0.8131156 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6656952 301.97 3.241398
115 EI1027 CD027 SI026 0.0000000 % 0 Figure 3 2011 Jun Jun Methods Study sites and biannual disease surveys p816, Figure 3 Geoffrey Bay, Magnetic Island Methods Study sites and biannual disease surveys p816 Western Pacific Australia -19.153980 146.86556 -19.153980 146.86556 Methods Study sites and biannual disease surveys p816, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, sample size 5 Pacific Ocean 22.92500 22.9250 NA 6 6 28.25267 28.085 0.4288319 win 12 winter 0.0000000 NA NA 0.20000 1 Methods Study sites and biannual disease surveys p816 NA 1 Mixed Montipora sp Belt 5 20.0000 2.00 40.0000 Methods Study sites and biannual disease surveys p817 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7796326 301.97 1.548555
116 EI0111 CD028 SI027 0.4523810 % 6.87E-02 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Coral Gardens Reef Crest Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.1190476 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
117 EI0112 CD028 SI027 0.0000000 % 0 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Coral Gardens Reef Flat Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.0000000 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
118 EI0113 CD028 SI027 0.0000000 % 0 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Coral Gardens Reef Slope Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.0000000 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
119 EI0114 CD028 SI027 0.0000000 % 0 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Hoga Buoy 2 Reef Crest Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.0000000 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
120 EI0115 CD028 SI027 1.3095238 % 0.98974332 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Hoga Buoy 2 Reef Flat Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 1.7142857 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
121 EI0116 CD028 SI027 0.0000000 % 0 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Hoga Buoy 2 Reef Slope Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.0000000 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
122 EI0117 CD028 SI027 0.2380952 % 0.13746435 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Kaledupa 1 Reef Crest Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.2380952 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
123 EI0118 CD028 SI027 2.7380952 % 1.05847549 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Kaledupa 1 Reef Flat Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 1.8333330 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
124 EI0119 CD028 SI027 0.3095238 % 0.26118226 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Kaledupa 1 Reef Slope Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.4523810 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
125 EI0120 CD028 SI027 0.5952381 % 0.316168 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Pak Kasims Reef Crest Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.5476190 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
126 EI0121 CD028 SI027 0.4523810 % 8.25E-02 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Pak Kasims Reef Flat Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.1428571 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
127 EI0122 CD028 SI027 0.1666667 % 0.15121078 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Pak Kasims Reef Slope Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.2619048 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
128 EI0123 CD028 SI027 0.6190476 % 0.2749287 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Sampela Reef Crest Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.4761905 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
129 EI0124 CD028 SI027 1.5476190 % 1.07222193 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Sampela Reef Flat Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 1.8571429 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
130 EI0125 CD028 SI027 0.0000000 % 0 Figure 8 2005 Jun-Sep Jun-Sep Materials and Methods Study site p404 Sampela Reef Slope Wakatobi Marine National Park Materials and Methods Study site p404, Figure 8 Coral Triangle & SE Asia Southeast Asia -5.563220 123.93047 -5.563220 123.93047 GoogleMaps Wakatobi National Park NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 27.89100 27.6180 0.3919173 6 9 29.31600 29.100 0.5752639 win 12 winter 0.0000000 NA NA 0.24000 15 Figure 8 12352 1 Coral number aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods Survey method p404 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study examines more diseases, but figure 8 where the effect size was extracted from only measured white syndrome 1.1628494 302.42 1.210006
131 EI0126 CD029 SI028 1.8974359 % 0.46153846 Figure 3 2010 Oct-Nov Oct-Nov Materials and methods Study sites p949 Coral Gardens Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.80400 29.8040 0.3478969 10 11 28.98600 28.663 0.7007514 spr 4 spring 1.3846154 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4614258 302.43 4.509983
132 EI0127 CD029 SI028 1.0000000 % 0.23076923 Figure 3 2011 Oct-Nov Oct-Nov Materials and methods Study sites p949 Coral Gardens Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.02150 29.0215 0.9425735 10 11 29.14200 28.913 0.7049695 spr 4 spring 0.6923077 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9624863 302.43 4.128520
133 EI0128 CD029 SI028 1.0769231 % 0.79487179 Figure 3 2010 Oct-Nov Oct-Nov Materials and methods Study sites p949 Hoga Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.80400 29.8040 0.3478969 10 11 28.98600 28.663 0.7007514 spr 4 spring 2.3846154 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4614258 302.43 4.509983
134 EI0129 CD029 SI028 0.3751051 % 0.15178669 Figure 3 2011 Oct-Nov Oct-Nov Materials and methods Study sites p949 Hoga Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.02150 29.0215 0.9425735 10 11 29.14200 28.913 0.7049695 spr 4 spring 0.4553601 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9624863 302.43 4.128520
135 EI0130 CD029 SI028 1.3465399 % 0.36428806 Figure 3 2010 Oct-Nov Oct-Nov Materials and methods Study sites p949 Kaledupa Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.80400 29.8040 0.3478969 10 11 28.98600 28.663 0.7007514 spr 4 spring 1.0928642 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4614258 302.43 4.509983
136 EI0131 CD029 SI028 0.3751051 % 0.12142935 Figure 3 2011 Oct-Nov Oct-Nov Materials and methods Study sites p949 Kaledupa Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.02150 29.0215 0.9425735 10 11 29.14200 28.913 0.7049695 spr 4 spring 0.3642881 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9624863 302.43 4.128520
137 EI0132 CD029 SI028 0.9670732 % 0.28839471 Figure 3 2010 Oct-Nov Oct-Nov Materials and methods Study sites p949 Pak Kasims Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.80400 29.8040 0.3478969 10 11 28.98600 28.663 0.7007514 spr 4 spring 0.8651841 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4614258 302.43 4.509983
138 EI0133 CD029 SI028 0.3902837 % 0.18214403 Figure 3 2011 Oct-Nov Oct-Nov Materials and methods Study sites p949 Pak Kasims Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.02150 29.0215 0.9425735 10 11 29.14200 28.913 0.7049695 spr 4 spring 0.5464321 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9624863 302.43 4.128520
139 EI0134 CD029 SI028 0.3751051 % 0.15178669 Figure 3 2010 Oct-Nov Oct-Nov Materials and methods Study sites p949 Sampela Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.80400 29.8040 0.3478969 10 11 28.98600 28.663 0.7007514 spr 4 spring 0.4553601 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4614258 302.43 4.509983
140 EI0135 CD029 SI028 0.7697505 % 0.24285871 Figure 3 2011 Oct-Nov Oct-Nov Materials and methods Study sites p949 Sampela Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=9 Pacific Ocean 29.02150 29.0215 0.9425735 10 11 29.14200 28.913 0.7049695 spr 4 spring 0.7285761 NA NA 0.72000 6 Materials and methods Study sites p949 NA 1 NA Belt 9 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 NA 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9624863 302.43 4.128520
141 EI0136 CD029 SI028 0.7242145 % 0.45536007 Figure 3 2010 Oct-Nov Oct-Nov Materials and methods Study sites p949 Blue Bowl Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=6 Pacific Ocean 29.80400 29.8040 0.3478969 10 11 28.98600 28.663 0.7007514 spr 4 spring 1.1153998 NA NA 0.14000 6 Materials and methods Study sites p949 NA 1 NA Belt 6 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 Blue Bowl has no reef flat transects 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4614258 302.43 4.509983
142 EI0137 CD029 SI028 5.0349565 % 3.09644851 Figure 3 2011 Oct-Nov Oct-Nov Materials and methods Study sites p949 Blue Bowl Wakatobi Marine National Park Figure 3 Coral Triangle & SE Asia Southeast Asia -5.468610 123.74468 -5.468610 123.74468 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=6 Pacific Ocean 29.02150 29.0215 0.9425735 10 11 29.14200 28.913 0.7049695 spr 4 spring 7.5847189 NA NA 0.14000 6 Materials and methods Study sites p949 NA 1 NA Belt 6 20.0000 4.00 80.0000 Materials and methods Survey method p949-950 Blue Bowl has no reef flat transects 9 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9624863 302.43 4.128520
143 EI0138 CD030 SI029 14.5000000 % 4 Results 3.1.1 Disease prevalence 2011 Sep Sep Methods Data collection Koh Tau Figure 1 Coral Triangle & SE Asia Southeast Asia 10.103090 99.83952 10.103090 99.83952 Figure 1, GoogleMaps NA NA High use site Pacific Ocean 28.76500 28.7650 NA 9 9 29.13267 29.180 0.0433172 aut 9 fall NA NA NA 0.45000 10 Methods Data collection 5983 1 NA Belt 15 15.0000 2.00 30.0000 Methods Data collection, Table 1 NA 4 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.8981934 303.28 0.000000
144 EI0139 CD030 SI029 5.2000000 % 1.3 Results 3.1.1 Disease prevalence 2011 Sep Sep Methods Data collection Koh Tau Figure 1 Coral Triangle & SE Asia Southeast Asia 10.103090 99.83952 10.103090 99.83952 Figure 1, GoogleMaps NA NA Low use site Pacific Ocean 28.76500 28.7650 NA 9 9 29.13267 29.180 0.0433172 aut 9 fall NA NA NA 0.45000 10 Methods Data collection 4516 1 NA Belt 15 15.0000 2.00 30.0000 Methods Data collection, Table 1 NA 4 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.8981934 303.28 0.000000
145 EI0140 CD031 SI030 1.0000000 % 0.3 Results 3a Influence of protected areas on coral disease following an acute disturbance 2012 Feb Feb Material and methods Study locations and protected areas management Palm Island Figure 1 Western Pacific Australia -18.566667 146.48333 -18.566667 146.48333 Material and methods Study locations and protected areas management NA NA Inside reserves Pacific Ocean 28.92800 28.9280 NA 2 2 28.38100 28.330 0.6599792 sum 8 summer NA NA NA 2.34000 26 Results 3a Influence of protected areas on coral disease following an acute disturbance 36104 1 Coral number aggregated from whole study Belt 3 15.0000 2.00 30.0000 Material and methods 2b Coral disease surveys and visual census of reef fishes Transect number is per site 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7178421 301.94 0.000000
146 EI0141 CD031 SI030 7.4000000 % 0.9 Results 3a Influence of protected areas on coral disease following an acute disturbance 2012 Feb Feb Material and methods Study locations and protected areas management Palm Island Figure 1 Western Pacific Australia -18.566667 146.48333 -18.566667 146.48333 Material and methods Study locations and protected areas management NA NA Outside reserves Pacific Ocean 28.92800 28.9280 NA 2 2 28.38100 28.330 0.6599792 sum 8 summer NA NA NA 2.34000 26 Results 3a Influence of protected areas on coral disease following an acute disturbance 36104 1 Coral number aggregated from whole study Belt 3 15.0000 2.00 30.0000 Material and methods 2b Coral disease surveys and visual census of reef fishes Transect number is per site 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7178421 301.94 0.000000
147 EI0142 CD031 SI031 1.4000000 % 0.7 Results 3b Influence of protected areas on coral disease following a chronic disturbance 2013 May May Material and methods Study locations and protected areas management Keppel Islands Figure 1 Western Pacific Australia -23.166670 150.95000 -23.166670 150.95000 Material and methods Study locations and protected areas management NA NA Inside reserves Pacific Ocean 24.49300 24.4930 NA 5 5 26.95367 27.350 0.4542198 aut 11 fall NA NA NA 1.89000 21 Results 3b Influence of protected areas on coral disease following a chronic disturbance 36104 1 Coral number aggregated from whole study Belt 3 15.0000 2.00 30.0000 Material and methods 2b Coral disease surveys and visual census of reef fishes Transect number is per site 3 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5424957 300.71 0.000000
148 EI0143 CD031 SI031 3.4000000 % 1 Results 3b Influence of protected areas on coral disease following a chronic disturbance 2013 May May Material and methods Study locations and protected areas management Keppel Islands Figure 1 Western Pacific Australia -23.166670 150.95000 -23.166670 150.95000 Material and methods Study locations and protected areas management NA NA Outside reserves Pacific Ocean 24.49300 24.4930 NA 5 5 26.95367 27.350 0.4542198 aut 11 fall NA NA NA 1.89000 21 Results 3b Influence of protected areas on coral disease following a chronic disturbance 36104 1 Coral number aggregated from whole study Belt 3 15.0000 2.00 30.0000 Material and methods 2b Coral disease surveys and visual census of reef fishes Transect number is per site 4 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5424957 300.71 0.000000
149 EI0144 CD032 SI032 1.0000000 % 0.2 Results Influence of marine protected areas on coral disease prevalence p2560 2012 Oct-Nov Oct-Nov Methods Study location and protected areas management p2557 Whitsunday Islands Figure 1 Western Pacific Australia -20.133330 148.93330 -20.133330 148.93330 Methods Study location and protected areas management p2556 NA NA Reserves Pacific Ocean 25.08000 25.0800 1.1030862 10 11 27.72200 27.758 0.6667287 spr 4 spring NA NA NA 1.89000 21 Methods Study location and protected areas management p2557 80866 1 Coral number aggregated from whole study Belt 3 15.0000 2.00 30.0000 Methods Coral health surveys p2257 Transect number is per site 6 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8328247 301.33 2.504247
150 EI0145 CD032 SI032 4.1000000 % 0.4 Results Influence of marine protected areas on coral disease prevalence p2560 2012 Oct-Nov Oct-Nov Methods Study location and protected areas management p2557 Whitsunday Islands Figure 1 Western Pacific Australia -20.133330 148.93330 -20.133330 148.93330 Methods Study location and protected areas management p2556 NA NA Non-reserve sites Pacific Ocean 25.08000 25.0800 1.1030862 10 11 27.72200 27.758 0.6667287 spr 4 spring NA NA NA 1.80000 20 Methods Study location and protected areas management p2557 80866 1 Coral number aggregated from whole study Belt 3 15.0000 2.00 30.0000 Methods Coral health surveys p2257 Transect number is per site 6 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8328247 301.33 2.504247
151 EI0146 CD033 SI033 3.2700000 % 0.62 Table 1 2009 Jun-July Jun-Jul Methods Study sites and data collection p1046 Great Barrier Reef Marine Park Figure 1 Western Pacific Australia -17.007790 152.16906 -17.007790 152.16906 Figure 1, GoogleMaps NA NA With platform (high tourism); renamed months (removed y) to match format for data analysis Pacific Ocean 25.74800 25.7480 0.3535534 6 7 28.42933 28.373 0.4551227 win 12 winter NA NA NA 0.72000 4 Figure 1 7043 1 NA Belt 24 15.0000 2.00 30.0000 Methods Study sites and data collection p1047 NA 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414001 301.28 9.218532
152 EI0147 CD033 SI033 0.2100000 % 7.00E-02 Table 1 2009 Jun-July Jun-Jul Methods Study sites and data collection p1046 Great Barrier Reef Marine Park Figure 1 Western Pacific Australia -17.007790 152.16906 -17.007790 152.16906 Figure 1, GoogleMaps NA NA Without platform (low tourism); renamed months (removed y) to match format for data analysis Pacific Ocean 25.74800 25.7480 0.3535534 6 7 28.42933 28.373 0.4551227 win 12 winter NA NA NA 0.63000 4 Figure 1 9468 1 NA Belt 21 15.0000 2.00 30.0000 Methods Study sites and data collection p1047 NA 3 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414001 301.28 9.218532
153 EI0148 CD035 SI034 0.0900000 % 0.04 Results Distribution and spatial variability of BBD p45 2004 Jan-Mar Jan-Mar Materials and Methods p43 Great Barrier Reef - Gladstone to Cooktown Figure 1 Western Pacific Australia -18.420030 147.44867 -18.420030 147.44867 Figure 1, GoogleMaps Dip Reef NA NA NA Pacific Ocean 29.11033 29.5280 0.4011693 1 3 28.33433 28.398 0.5801265 sum 7 summer NA NA NA 2.28000 19 Table 1 113747 1 NA Belt 3 20.0000 2.00 40.0000 Materials and Methods p43 Transect number is per site 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9564285 301.61 0.000000
154 EI0149 CD036 SI035 2.2900000 % 0.45 Table 3 2005 Jan Jan Materials and Methods Quantifying coral disease and coral community structure p137 Palau Figure 1 Coral Triangle & SE Asia Southeast Asia 7.337981 134.55450 7.337981 134.55450 Figure 1, GoogleMaps Palau NA NA MPA Pacific Ocean 28.28300 28.2830 NA 1 1 29.08100 29.228 0.3747839 win 1 winter NA NA NA 0.96000 8 Materials and Methods Quantifying coral disease and coral community structure p137 NA 1 NA Belt 24 20.0000 2.00 40.0000 Materials and Methods Quantifying coral disease and coral community structure p138 NA 3 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2271423 302.40 0.000000
155 EI0150 CD036 SI035 1.8300000 % 0.44 Table 3 2005 Jan Jan Materials and Methods Quantifying coral disease and coral community structure p137 Palau Figure 1 Coral Triangle & SE Asia Southeast Asia 7.337981 134.55450 7.337981 134.55450 Figure 1, GoogleMaps Palau NA NA Control Pacific Ocean 28.28300 28.2830 NA 1 1 29.08100 29.228 0.3747839 win 1 winter NA NA NA 0.96000 8 Materials and Methods Quantifying coral disease and coral community structure p137 NA 1 NA Belt 24 20.0000 2.00 40.0000 Materials and Methods Quantifying coral disease and coral community structure p138 NA 3 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2271423 302.40 0.000000
156 EI0151 CD037 SI036 6.9767440 % NA Figure 2 2004 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 26.87984 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.12800 28.1280 NA 10 10 29.64467 29.963 0.6671268 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
157 EI0152 CD037 SI036 6.5116280 % NA Figure 2 2004 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 25.97700 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.12800 28.1280 NA 10 10 29.64467 29.963 0.6671268 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
158 EI0153 CD037 SI036 6.3105680 % NA Figure 2 2004 Nov Nov Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 26.87984 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 26.81300 26.8130 NA 11 11 29.64467 29.963 0.6671268 aut 11 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
159 EI0154 CD037 SI036 4.7778520 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 21.31235 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
160 EI0155 CD037 SI036 2.6463630 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 20.40951 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
161 EI0156 CD037 SI036 5.6304480 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 22.36566 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
162 EI0157 CD037 SI036 6.9093410 % NA Figure 2 2005 Apr Apr Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 23.11802 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 24.76000 24.7600 NA 4 4 29.42034 29.525 1.0588871 spr 4 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7864532 302.82 3.788568
163 EI0158 CD037 SI036 2.6463630 % NA Figure 2 2005 Aug Aug Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.69496 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 30.42300 30.4230 NA 8 8 29.42034 29.525 1.0588871 sum 8 summer NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7957153 302.82 3.902856
164 EI0159 CD037 SI036 1.3674700 % NA Figure 2 2005 Aug Aug Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.69496 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 30.42300 30.4230 NA 8 8 29.42034 29.525 1.0588871 sum 8 summer NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7957153 302.82 3.902856
165 EI0160 CD037 SI036 1.3674700 % NA Figure 2 2005 Sep Sep Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.24354 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 29.67500 29.6750 NA 9 9 29.42034 29.525 1.0588871 aut 9 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
166 EI0161 CD037 SI036 2.2200660 % NA Figure 2 2005 Sep Sep Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 30.34071 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 29.67500 29.6750 NA 9 9 29.42034 29.525 1.0588871 aut 9 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
167 EI0162 CD037 SI036 9.8934260 % NA Figure 2 2005 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 29.28740 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.43800 28.4380 NA 10 10 29.42034 29.525 1.0588871 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
168 EI0163 CD037 SI036 14.1564030 % NA Figure 2 2005 Nov Nov Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 25.37511 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 26.46800 26.4680 NA 11 11 29.42034 29.525 1.0588871 aut 11 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
169 EI0164 CD037 SI036 6.0567460 % NA Figure 2 2006 Mar Mar Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 22.66660 Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 24.15500 24.1550 NA 3 3 29.26933 29.395 0.7494438 spr 3 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8278503 302.82 5.594270
170 EI0165 CD037 SI036 7.7619370 % NA Figure 2 2006 Apr Apr Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps NA Figure 2 White plague; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 25.32500 25.3250 NA 4 4 29.26933 29.395 0.7494438 spr 4 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 96 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8278503 302.82 5.594270
171 EI0166 CD037 SI036 1.5400055 % NA Figure 2 2004 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 26.87984 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.12800 28.1280 NA 10 10 29.64467 29.963 0.6671268 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
172 EI0167 CD037 SI036 0.7122093 % NA Figure 2 2004 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 25.97700 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.12800 28.1280 NA 10 10 29.64467 29.963 0.6671268 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
173 EI0168 CD037 SI036 1.5400055 % NA Figure 2 2004 Nov Nov Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 26.87984 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 26.81300 26.8130 NA 11 11 29.64467 29.963 0.6671268 aut 11 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
174 EI0169 CD037 SI036 2.3678018 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 21.31235 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
175 EI0170 CD037 SI036 1.5400055 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 20.40951 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
176 EI0171 CD037 SI036 1.5400055 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 22.36566 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
177 EI0172 CD037 SI036 1.5400055 % NA Figure 2 2005 Apr Apr Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 23.11802 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 24.76000 24.7600 NA 4 4 29.42034 29.525 1.0588871 spr 4 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7864532 302.82 3.788568
178 EI0173 CD037 SI036 4.8511905 % NA Figure 2 2005 Aug Aug Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.69496 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 30.42300 30.4230 NA 8 8 29.42034 29.525 1.0588871 sum 8 summer NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7957153 302.82 3.902856
179 EI0174 CD037 SI036 7.3345792 % NA Figure 2 2005 Aug Aug Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.69496 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 30.42300 30.4230 NA 8 8 29.42034 29.525 1.0588871 sum 8 summer NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7957153 302.82 3.902856
180 EI0175 CD037 SI036 6.9206811 % NA Figure 2 2005 Sep Sep Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.24354 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 29.67500 29.6750 NA 9 9 29.42034 29.525 1.0588871 aut 9 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
181 EI0176 CD037 SI036 6.5067829 % NA Figure 2 2005 Sep Sep Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 30.34071 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 29.67500 29.6750 NA 9 9 29.42034 29.525 1.0588871 aut 9 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
182 EI0177 CD037 SI036 5.6789867 % NA Figure 2 2005 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 29.28740 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.43800 28.4380 NA 10 10 29.42034 29.525 1.0588871 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
183 EI0178 CD037 SI036 4.0233942 % NA Figure 2 2005 Nov Nov Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 25.37511 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 26.46800 26.4680 NA 11 11 29.42034 29.525 1.0588871 aut 11 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
184 EI0179 CD037 SI036 0.2983112 % NA Figure 2 2006 Mar Mar Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 22.66660 Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 24.15500 24.1550 NA 3 3 29.26933 29.395 0.7494438 spr 3 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8278503 302.82 5.594270
185 EI0180 CD037 SI036 0.2983112 % NA Figure 2 2006 Apr Apr Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps NA Figure 2 Dark spot; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 25.32500 25.3250 NA 4 4 29.26933 29.395 0.7494438 spr 4 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 172 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8278503 302.82 5.594270
186 EI0181 CD037 SI036 0.0000000 % NA Figure 2 2004 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 26.87984 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.12800 28.1280 NA 10 10 29.64467 29.963 0.6671268 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
187 EI0182 CD037 SI036 0.0000000 % NA Figure 2 2004 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 25.97700 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.12800 28.1280 NA 10 10 29.64467 29.963 0.6671268 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
188 EI0183 CD037 SI036 0.0000000 % NA Figure 2 2004 Nov Nov Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 26.87984 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 26.81300 26.8130 NA 11 11 29.64467 29.963 0.6671268 aut 11 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0271301 302.82 3.788568
189 EI0184 CD037 SI036 0.0000000 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 21.31235 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
190 EI0185 CD037 SI036 0.0000000 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 20.40951 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
191 EI0186 CD037 SI036 0.0000000 % NA Figure 2 2005 Feb Feb Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 22.36566 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 23.40500 23.4050 NA 2 2 29.42034 29.525 1.0588871 win 2 winter NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2014542 302.82 3.788568
192 EI0187 CD037 SI036 0.0000000 % NA Figure 2 2005 Apr Apr Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 23.11802 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 24.76000 24.7600 NA 4 4 29.42034 29.525 1.0588871 spr 4 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7864532 302.82 3.788568
193 EI0188 CD037 SI036 2.3809520 % NA Figure 2 2005 Aug Aug Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.69496 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 30.42300 30.4230 NA 8 8 29.42034 29.525 1.0588871 sum 8 summer NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7957153 302.82 3.902856
194 EI0189 CD037 SI036 4.2857140 % NA Figure 2 2005 Aug Aug Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.69496 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 30.42300 30.4230 NA 8 8 29.42034 29.525 1.0588871 sum 8 summer NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7957153 302.82 3.902856
195 EI0190 CD037 SI036 6.1904760 % NA Figure 2 2005 Sep Sep Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 31.24354 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 29.67500 29.6750 NA 9 9 29.42034 29.525 1.0588871 aut 9 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
196 EI0191 CD037 SI036 10.4761900 % NA Figure 2 2005 Sep Sep Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 30.34071 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 29.67500 29.6750 NA 9 9 29.42034 29.525 1.0588871 aut 9 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
197 EI0192 CD037 SI036 19.0476190 % NA Figure 2 2005 Oct Oct Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 29.28740 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 28.43800 28.4380 NA 10 10 29.42034 29.525 1.0588871 aut 10 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
198 EI0193 CD037 SI036 2.3809520 % NA Figure 2 2005 Nov Nov Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 25.37511 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 26.46800 26.4680 NA 11 11 29.42034 29.525 1.0588871 aut 11 fall NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7264404 302.82 5.594270
199 EI0194 CD037 SI036 2.3809520 % NA Figure 2 2006 Mar Mar Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps 22.66660 Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 24.15500 24.1550 NA 3 3 29.26933 29.395 0.7494438 spr 3 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8278503 302.82 5.594270
200 EI0195 CD037 SI036 12.8571430 % NA Figure 2 2006 Apr Apr Figure 2 Florida Keys, USA Materials and Methods p2860 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.866528 -80.67167 24.866528 -80.67167 Materials and Methods p2860, GoogleMaps NA Figure 2 Black band; Prevalence extracted using MetaDigitise in Rstudio, n=10 Atlantic Ocean 25.32500 25.3250 NA 4 4 29.26933 29.395 0.7494438 spr 4 spring NA 0.10 NA 0.16000 2 Materials and Methods p2860 28 0 NA Quadrat 10 4.0000 4.00 16.0000 Materials and Methods p2860 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8278503 302.82 5.594270
201 EI0196 CD038 SI037 6.9417476 % 2.330097 Figure 3b 2010 Sep Sep Figure 3b St Thomas Island, US Virgin Islands Materials and Methods Study Site Caribbean & Gulf of Mexico Caribbean/Atlantic 18.344139 -64.98239 18.344139 -64.98239 Materials and Methods Study Site, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=6 Atlantic Ocean 29.35800 29.3580 NA 9 9 29.16267 29.105 0.4016176 aut 9 fall 5.7075490 NA NA 0.06000 1 Materials and Methods Study site NA 0 NA Belt 6 10.0000 1.00 10.0000 Materials and Methods Field data collection methods, Figure 3b NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VI-MRTL 0.4828491 301.61 5.242833
202 EI0197 CD038 SI037 1.2135922 % 1.262136 Figure 3b 2010 Oct Oct Figure 3b St Thomas Island, US Virgin Islands Materials and Methods Study Site Caribbean & Gulf of Mexico Caribbean/Atlantic 18.344139 -64.98239 18.344139 -64.98239 Materials and Methods Study Site, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Atlantic Ocean 29.02000 29.0200 NA 10 10 29.16267 29.105 0.4016176 aut 10 fall 2.1860840 NA NA 0.03000 1 Materials and Methods Study site NA 0 NA Belt 3 10.0000 1.00 10.0000 Materials and Methods Field data collection methods, Figure 3b NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VI-MRTL 0.4828491 301.61 4.218555
203 EI0198 CD038 SI037 4.1262136 % 2.378641 Figure 3b 2010 Oct Oct Figure 3b St Thomas Island, US Virgin Islands Materials and Methods Study Site Caribbean & Gulf of Mexico Caribbean/Atlantic 18.344139 -64.98239 18.344139 -64.98239 Materials and Methods Study Site, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Atlantic Ocean 29.02000 29.0200 NA 10 10 29.16267 29.105 0.4016176 aut 10 fall 4.1199270 NA NA 0.03000 1 Materials and Methods Study site NA 0 NA Belt 3 10.0000 1.00 10.0000 Materials and Methods Field data collection methods, Figure 3b NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VI-MRTL 0.4828491 301.61 4.218555
204 EI0199 CD038 SI037 1.6019417 % 1.747573 Figure 3b 2010 Nov Nov Figure 3b St Thomas Island, US Virgin Islands Materials and Methods Study Site Caribbean & Gulf of Mexico Caribbean/Atlantic 18.344139 -64.98239 18.344139 -64.98239 Materials and Methods Study Site, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=3 Atlantic Ocean 28.13800 28.1380 NA 11 11 29.16267 29.105 0.4016176 aut 11 fall 3.0268850 NA NA 0.03000 1 Materials and Methods Study site NA 0 NA Belt 3 10.0000 1.00 10.0000 Materials and Methods Field data collection methods, Figure 3b NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VI-MRTL 0.4828491 301.61 4.218555
205 EI0200 CD038 SI037 0.0000000 % 0 Figure 3b 2011 Feb Feb Figure 3b St Thomas Island, US Virgin Islands Materials and Methods Study Site Caribbean & Gulf of Mexico Caribbean/Atlantic 18.344139 -64.98239 18.344139 -64.98239 Materials and Methods Study Site, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n=7 Atlantic Ocean 26.10800 26.1080 NA 2 2 28.73200 28.688 0.2370824 win 2 winter 0.0000000 NA NA 0.07000 1 Materials and Methods Study site NA 0 NA Belt 7 10.0000 1.00 10.0000 Materials and Methods Field data collection methods, Figure 3b NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VI-MRTL 0.4335632 301.61 4.218555
206 EI0201 CD039 SI038 0.0000000 % 0 Figure 4 2013 Feb-Aug Feb-Aug Material and methods 2a Study site and species p2 Lizard Island, GBR Material and methods 2a Study site and species p2 Western Pacific Australia -14.692154 145.45094 -14.692154 145.45094 Figure S1, GoogleMaps NA NA Outside damselfish territory; n = 12 Pacific Ocean 26.58314 26.3850 2.0612886 2 8 28.76033 29.125 0.3214601 multi 8 summer NA NA NA 0.06000 4 Materials and methods 2d Coral disease surveys p3 NA 0 NA Belt-Quadrat 60 1.0000 1.00 1.0000 Materials and methods 2d Coral disease surveys p3 Transect data is calculated based on quadrats (transect exists as a reference for where to place quadrats for collecting data); n for se is still 12 for number of transects (ignores that there are 5 quadrats per transect) 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6192932 301.73 0.000000
207 EI0202 CD039 SI038 3.1700000 % 1.41 Results 3c Coral disease surveys p5 2013 Feb-Aug Feb-Aug Material and methods 2a Study site and species p2 Lizard Island, GBR Material and methods 2a Study site and species p2 Western Pacific Australia -14.692154 145.45094 -14.692154 145.45094 Figure S1, GoogleMaps NA NA Inside damselfish territory; n = 12 Pacific Ocean 26.58314 26.3850 2.0612886 2 8 28.76033 29.125 0.3214601 multi 8 summer NA NA NA 0.06000 4 Materials and methods 2d Coral disease surveys p3 NA 0 NA Belt-Quadrat 60 1.0000 1.00 1.0000 Materials and methods 2d Coral disease surveys p3 Transect data is calculated based on quadrats (transect exists as a reference for where to place quadrats for collecting data); n for se is still 12 for number of transects (ignores that there are 5 quadrats per transect) 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6192932 301.73 0.000000
208 EI0203 CD040 SI039 0.1090909 % 9.09E-02 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Bird Islet, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
209 EI0204 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Lizard Head, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
210 EI0205 CD040 SI039 0.1272727 % 0.10909091 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design North Reef, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
211 EI0206 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design South Island, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
212 EI0207 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Washing Machine, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
213 EI0208 CD040 SI039 2.3272727 % 1.52727273 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Channel, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
214 EI0209 CD040 SI039 3.4181818 % 0.21818182 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Ghost Beach, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
215 EI0210 CD040 SI039 0.4181818 % 0.21818182 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Loomis Reef, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
216 EI0211 CD040 SI039 0.6545455 % 0.30909091 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Loomis Beach, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA NA Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
217 EI0212 CD040 SI039 3.4181818 % 1.8 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Palfrey, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
218 EI0213 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Casuarina, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
219 EI0214 CD040 SI039 1.7818182 % 0.89090909 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Horseshoe, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
220 EI0215 CD040 SI039 0.3272727 % 0.18181818 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Little Vickys, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
221 EI0216 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Mushroom, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
222 EI0217 CD040 SI039 0.1818182 % 0.14545455 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Vickies, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Brown band disease; Prevalence extracted using MetaDigitise in R, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer NA NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
223 EI0218 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Bird Islet, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
224 EI0219 CD040 SI039 0.1997439 % 0.1649168 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Lizard Head, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.2856442 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
225 EI0220 CD040 SI039 0.2563380 % 0.1177977 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design North Reef, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.2040316 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
226 EI0221 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design South Island, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
227 EI0222 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Washing Machine, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
228 EI0223 CD040 SI039 0.7157490 % 0.6714469 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Channel, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 1.1629801 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
229 EI0224 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Ghost Beach, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
230 EI0225 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Loomis Reef, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
231 EI0226 CD040 SI039 0.5154930 % 0.2473752 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Loomis Beach, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.4284663 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
232 EI0227 CD040 SI039 1.7288092 % 1.6727273 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Palfrey, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 2.8972486 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
233 EI0228 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Casuarina, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
234 EI0229 CD040 SI039 1.5638924 % 0.7892446 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Horseshoe, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 1.3670117 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
235 EI0230 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Little Vickys, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
236 EI0231 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Mushroom, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
237 EI0232 CD040 SI039 0.0000000 % 0 Figure 3a 2009 Dec-Feb Dec-Feb Materials and methods Study site and sampling design Vickies, Lizard Island, GBR Figure 3a, Materials and methods Study site and sampling design Western Pacific Australia -14.666670 145.45000 -14.666670 145.45000 Materials and methods Study site and sampling design, GoogleMaps NA NA Black band disease; Prevalence extracted using MetaDigitise in Rstudio, n=3 Pacific Ocean 28.78367 28.6180 0.3474996 12 2 28.78367 28.618 0.3474996 aut 6 summer 0.0000000 NA NA 0.12000 1 Figure 3a NA 1 Effect sizes are per site Belt 3 20.0000 2.00 40.0000 Materials and methods Study site and sampling design p474 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3457031 301.74 1.044272
238 EI0233 CD041 SI040 2.7000000 % NA Figure 3a 1999 Jun Jun Methods p668 Little Cayman Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.684933 -80.07788 19.684933 -80.07788 Table 1 Mixing Bowl, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 73 Atlantic Ocean 28.68500 28.6850 NA 6 6 29.18267 29.028 0.5903948 sum 6 summer NA NA NA 0.73000 5 Table 1 821 1 NA Line 73 10.0000 1.00 10.0000 Methods p668 NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 3b 0.7939529 302.61 3.505726
239 EI0234 CD041 SI040 6.0000000 % NA Figure 3a 2002 Jul-Aug Jul-Aug Methods p668 Little Cayman Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.684933 -80.07788 19.684933 -80.07788 Table 1 Mixing Bowl, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 41 Atlantic Ocean 29.68150 29.6815 0.5069949 7 8 29.33367 29.323 0.7010611 sum 7 summer NA NA NA 0.41000 4 Table 1 516 1 NA Line 41 10.0000 1.00 10.0000 Methods p668 NA 3 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Figure 3b 0.3357086 302.61 0.000000
240 EI0235 CD041 SI040 4.6000000 % NA Figure 3a 2003 Jul-Aug Jul-Aug Methods p668 Little Cayman Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.684933 -80.07788 19.684933 -80.07788 Table 1 Mixing Bowl, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 36 Atlantic Ocean 29.34400 29.3440 0.3549668 7 8 29.18867 29.093 0.3679483 sum 7 summer NA NA NA 0.36000 4 Table 1 501 1 NA Line 36 10.0000 1.00 10.0000 Methods p668 NA 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 3b 0.4328613 302.61 0.000000
241 EI0236 CD041 SI040 6.7500000 % NA Figure 3a 2004 Jul-Aug Jul-Aug Methods p668 Little Cayman Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.684933 -80.07788 19.684933 -80.07788 Table 1 Mixing Bowl, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 88 Atlantic Ocean 29.49900 29.4990 0.2955715 7 8 29.21867 29.290 0.5286226 sum 7 summer NA NA NA 0.88000 9 Table 1 1125 1 NA Line 88 10.0000 1.00 10.0000 Methods p668 NA 4 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Figure 3b 0.6993103 302.61 0.000000
242 EI0237 CD042 SI041 0.0200000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Shallow sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0400000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
243 EI0238 CD042 SI041 0.0200000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Shallow sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0400000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
244 EI0239 CD042 SI041 0.3200000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Shallow sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.3000000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
245 EI0240 CD042 SI041 2.9700000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Shallow sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 1.7000000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
246 EI0241 CD042 SI041 0.0000000 % 0 Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Shallow sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0000000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “Yellow Blotch” in paper marked as ywllow band 0.5457077 301.14 8.795700
247 EI0242 CD042 SI041 0.0200000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Deep sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0400000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
248 EI0243 CD042 SI041 0.0200000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Deep sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0400000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
249 EI0244 CD042 SI041 0.0000000 % 0 Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Deep sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0000000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
250 EI0245 CD042 SI041 5.0300000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Deep sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 1.9500000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5457077 301.14 8.795700
251 EI0246 CD042 SI041 0.0700000 % NA Table 3 2000 Aug Aug Materials and Methods p40 Madrizqui Key, Los Roques Archipelago Materials and Methods p40 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.936455 -66.67740 11.936455 -66.67740 Materials and Methods p40, GoogleMaps NA NA Deep sites relative percent of diseased colonies Atlantic Ocean 27.75000 27.7500 NA 8 8 27.08034 26.878 0.5948922 sum 8 summer 0.0700000 NA NA 0.60000 2 Materials and Methods p40 1439 1 NA Belt 3 50.0000 2.00 100.0000 Materials and Methods p40 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “Yellow Blotch” in paper marked as ywllow band 0.5457077 301.14 8.795700
256 EI0251 CD009 SI043 28.6000000 % NA Results p36 2002 Jun-Aug Jun-Aug Materials and methods Study site p35 Akumal Bay, Quintana Roo, Mexican Caribbean Materials and methods Study site p34 Caribbean & Gulf of Mexico Caribbean/Atlantic 20.391256 -87.31472 20.391256 -87.31472 GoogleMaps Akumal Bay 29.73000 Table 1 18.4–41.5% 95% CI Atlantic Ocean 29.11933 29.0730 0.3895717 6 8 29.11933 29.073 0.3895717 sum 6 summer NA NA 0.9333 1.44000 1 Figure 1 NA 0 NA Belt 4 60.0000 6.00 360.0000 Materials and methods Sampling and study design p35 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1728516 301.86 0.000000
257 EI0252 CD011 SI044 5.0862070 % 2.465517 Figure 2 2007 Jan Jan p293 Pedra de Leste, Abrolhos Bank, eastern Brazil Figure 1 XXXX Caribbean/Atlantic -17.959660 -38.70108 -17.959660 -38.70108 GoogleMaps Abrolhos Marine National Park 27.40000 p293 Prevalence extracted using MetaDigitise in Rstudio, n = 4; This region is not recognized in Hoegh-Guldberg map Atlantic Ocean 27.23300 27.2330 NA 1 1 26.98500 27.005 0.5352811 sum 7 summer 4.9310340 NA NA 0.04000 1 p293 NA 0 NA Belt 4 10.0000 1.00 10.0000 p293 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “White plague-like disease” categorized as white syndrome 0.6721191 300.09 0.000000
258 EI0253 CD011 SI044 3.3620690 % 1.931034 Figure 2 2007 Jul Jul p293 Pedra de Leste, Abrolhos Bank, eastern Brazil Figure 1 XXXX Caribbean/Atlantic -17.959660 -38.70108 -17.959660 -38.70108 GoogleMaps Abrolhos Marine National Park 27.40000 p293 Prevalence extracted using MetaDigitise in Rstudio, n = 4; This region is not recognized in Hoegh-Guldberg map Atlantic Ocean 25.25800 25.2580 NA 7 7 26.98500 27.005 0.5352811 win 1 winter 3.8620690 NA NA 0.04000 1 p293 NA 0 NA Belt 4 10.0000 1.00 10.0000 p293 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “White plague-like disease” categorized as white syndrome 0.1371307 300.09 0.000000
259 EI0254 CD011 SI044 7.4137930 % 2.465517 Figure 2 2007 Jan Jan p293 Timbebas, Abrolhos Bank, eastern Brazil Figure 1 XXXX Caribbean/Atlantic -17.959660 -38.70108 -17.959660 -38.70108 GoogleMaps Abrolhos Marine National Park 27.40000 p293 Prevalence extracted using MetaDigitise in Rstudio, n = 4; This region is not recognized in Hoegh-Guldberg map Atlantic Ocean 27.23300 27.2330 NA 1 1 26.98500 27.005 0.5352811 sum 7 summer 4.9310340 NA NA 0.04000 1 p293 NA 0 NA Belt 4 10.0000 1.00 10.0000 p293 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “White plague-like disease” categorized as white syndrome 0.6721191 300.09 0.000000
260 EI0255 CD011 SI044 1.3275860 % 1.068966 Figure 2 2007 Jul Jul p293 Timbebas, Abrolhos Bank, eastern Brazil Figure 1 XXXX Caribbean/Atlantic -17.959660 -38.70108 -17.959660 -38.70108 GoogleMaps Abrolhos Marine National Park 27.40000 p293 Prevalence extracted using MetaDigitise in Rstudio, n = 4; This region is not recognized in Hoegh-Guldberg map Atlantic Ocean 25.25800 25.2580 NA 7 7 26.98500 27.005 0.5352811 win 1 winter 2.1379310 NA NA 0.04000 1 p293 NA 0 NA Belt 4 10.0000 1.00 10.0000 p293 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “White plague-like disease” categorized as white syndrome 0.1371307 300.09 0.000000
261 EI0256 CD015 SI045 38.4920600 % NA Figure 5 2010 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.50290 29.1575 3.1298983 2 11 29.43000 29.310 0.4960097 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.0906982 302.58 0.000000
262 EI0257 CD015 SI045 41.6666700 % NA Figure 5 2011 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.91100 28.7740 2.5221626 2 11 29.19867 29.343 0.9299391 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.2971497 302.58 0.000000
263 EI0258 CD015 SI045 53.1746000 % NA Figure 5 2012 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.87460 28.3880 2.1884169 2 11 28.78800 29.063 0.9672772 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.4746475 302.58 2.218572
264 EI0259 CD015 SI045 20.6349200 % NA Figure 5 2013 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.74260 27.9090 2.2387222 2 11 28.32433 28.273 0.8062260 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.5521393 302.58 0.000000
265 EI0260 CD015 SI045 51.9841300 % NA Figure 5 2014 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 27.18530 28.7290 2.1712860 2 11 29.15867 29.498 1.0701410 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.6310654 302.58 0.000000
266 EI0261 CD015 SI045 53.5714300 % NA Figure 5 2015 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 27.63430 29.2255 1.9786979 2 11 29.43200 29.488 0.4436586 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.7693024 302.58 2.238561
267 EI0262 CD015 SI045 39.6825400 % NA Figure 5 2016 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods BCA Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 64; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 27.31800 29.3500 2.4052447 2 11 29.55100 30.095 0.8223307 multi 2 winter NA NA NA 0.44800 1 Materials and Methods NA 0 Effect sizes are per site Line 64 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.5317917 302.58 0.000000
268 EI0263 CD015 SI045 78.9682500 % NA Figure 6 2010 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.50290 29.1575 3.1298983 2 11 29.43000 29.310 0.4960097 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.0906982 302.58 0.000000
269 EI0264 CD015 SI045 62.3015900 % NA Figure 6 2011 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.91100 28.7740 2.5221626 2 11 29.19867 29.343 0.9299391 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.2971497 302.58 0.000000
270 EI0265 CD015 SI045 68.6507900 % NA Figure 6 2012 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.87460 28.3880 2.1884169 2 11 28.78800 29.063 0.9672772 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.4746475 302.58 2.218572
271 EI0266 CD015 SI045 39.6825400 % NA Figure 6 2013 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 26.74260 27.9090 2.2387222 2 11 28.32433 28.273 0.8062260 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.5521393 302.58 0.000000
272 EI0267 CD015 SI045 55.9523800 % NA Figure 6 2014 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 27.18530 28.7290 2.1712860 2 11 29.15867 29.498 1.0701410 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.6310654 302.58 0.000000
273 EI0268 CD015 SI045 73.0158700 % NA Figure 6 2015 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 27.63430 29.2255 1.9786979 2 11 29.43200 29.488 0.4436586 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.7693024 302.58 2.238561
274 EI0269 CD015 SI045 72.2222200 % NA Figure 6 2016 Feb-Mar, Jun-Aug, Oct-Nov Feb-Mar, Jun-Aug, Oct-Nov Materials and Methods Scooter Patch, Southeast Florida Materials and Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 26.166320 -80.04526 26.166320 -80.04526 GoogleMaps Broward County, FL NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 62; paper includes SE in written material for some measurements, but not in graphs Atlantic Ocean 27.31800 29.3500 2.4052447 2 11 29.55100 30.095 0.8223307 multi 2 winter NA NA NA 0.43400 1 Materials and Methods NA 0 Effect sizes are per site Line 62 7.0000 1.00 7.0000 Materials and Methods NA 2 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pooled in paper as “white disease” 0.5317917 302.58 0.000000
275 EI0284 CD020 SI046 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Marker 32, Yanbu, Red Sea Table 1 Western Indian Ocean Middle East 23.866400 37.89130 23.866400 37.89130 Table 1 NA NA NA Indian Ocean 29.23050 29.2305 1.2692567 10 11 29.58367 29.770 1.5011987 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 726 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7028351 303.04 0.000000
276 EI0285 CD020 SI047 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Marker 35, Yanbu, Red Sea Table 1 Western Indian Ocean Middle East 23.820700 37.93500 23.820700 37.93500 Table 1 NA NA NA Indian Ocean 29.23050 29.2305 1.2692567 10 11 29.58367 29.770 1.5011987 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 930 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7257004 303.08 0.000000
277 EI0286 CD020 SI048 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Abu Galaba, Yanbu, Red Sea Table 1 Western Indian Ocean Middle East 23.789100 37.93930 23.789100 37.93930 Table 1 NA NA NA Indian Ocean 29.23050 29.2305 1.2692567 10 11 29.58367 29.770 1.5011987 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 848 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7285690 303.15 0.000000
278 EI0287 CD020 SI049 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Fringing reef 1, Yanbu, Red Sea Table 1 Western Indian Ocean Middle East 24.136200 37.93960 24.136200 37.93960 Table 1 NA NA NA Indian Ocean 29.01400 29.0140 1.2883489 10 11 29.52700 29.683 1.4572761 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 1308 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 3.3060608 302.80 1.300007
279 EI0288 CD020 SI050 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Marker 10, Yanbu, Red Sea Table 1 Western Indian Ocean Middle East 24.018900 37.96660 24.018900 37.96660 Table 1 NA NA NA Indian Ocean 29.01400 29.0140 1.2883489 10 11 29.52700 29.683 1.4572761 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 1749 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.9114532 302.86 1.298570
280 EI0289 CD020 SI051 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Fringing reef 2, Yanbu, Red Sea Table 1 Western Indian Ocean Middle East 24.145200 37.91490 24.145200 37.91490 Table 1 NA NA NA Indian Ocean 29.01400 29.0140 1.2883489 10 11 29.52700 29.683 1.4572761 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 1830 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 3.3060608 302.80 1.300007
281 EI0290 CD020 SI052 0.0500000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Abu Madafi, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.076600 38.77510 22.076600 38.77510 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 2184 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9992828 303.88 0.000000
282 EI0291 CD020 SI053 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Al Fahal, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.111900 38.84110 22.111900 38.84110 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 6000 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9107361 303.89 0.000000
283 EI0292 CD020 SI054 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Al-Mashpah, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.077200 38.77440 22.077200 38.77440 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 4200 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9992828 303.88 0.000000
284 EI0293 CD020 SI055 0.0100000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Inner Fsar, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.235800 39.03040 22.235800 39.03040 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 6852 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7371521 303.90 0.000000
285 EI0294 CD020 SI056 0.0200000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Shaab, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.201200 38.99920 22.201200 38.99920 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 5778 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7471619 303.91 0.000000
286 EI0295 CD020 SI057 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Shi’b Nazar, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.340900 38.85210 22.340900 38.85210 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 3294 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2971344 303.92 0.000000
287 EI0296 CD020 SI058 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Tahlah, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.275000 39.04970 22.275000 39.04970 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 3780 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7753372 303.90 0.000000
288 EI0297 CD020 SI059 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Qita al Kirsh, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.425700 38.99570 22.425700 38.99570 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 5748 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8378448 303.88 0.000000
289 EI0298 CD020 SI060 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Um Alkthal, Thuwal, Red Sea Table 1 Western Indian Ocean Middle East 22.165300 38.93910 22.165300 38.93910 Table 1 NA NA NA Indian Ocean 29.84050 29.8405 1.1702622 10 11 29.81200 29.983 1.4262102 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 5208 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7585526 303.90 0.000000
290 EI0299 CD020 SI061 0.2100000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys La Plage, Jeddah, Red Sea Table 1 Western Indian Ocean Middle East 21.709200 39.08320 21.707160 38.80293 Table 1 NA NA coordinates not found in SST dataset - reselected from nearby coordinates on GoogleMaps Indian Ocean 30.21750 30.2175 1.0500527 10 11 30.01467 30.148 1.3499478 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 474 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9700012 303.87 0.000000
291 EI0300 CD020 SI062 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Abu Lath, Al-Lith, Red Sea Table 1 Western Indian Ocean Middle East 19.955400 40.15430 19.955400 40.15430 Table 1 NA NA NA Indian Ocean 31.33050 31.3305 0.7318554 10 11 30.54600 30.505 0.6394867 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 5556 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1789246 304.32 3.622857
292 EI0301 CD020 SI063 0.0300000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys South Reef, Al-Lith, Red Sea Table 1 Western Indian Ocean Middle East 19.899850 40.15140 19.899850 40.15140 Table 1 NA NA NA Indian Ocean 31.33050 31.3305 0.7318554 10 11 30.54600 30.505 0.6394867 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 3720 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1382217 304.35 1.017127
293 EI0302 CD020 SI064 0.0000000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Al-Lith 3, Al-Lith, Red Sea Table 1 Western Indian Ocean Middle East 19.860800 40.22820 19.860800 40.22820 Table 1 NA NA NA Indian Ocean 31.33050 31.3305 0.7318554 10 11 30.54600 30.505 0.6394867 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 4320 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3139267 304.36 1.071428
294 EI0303 CD020 SI065 0.0200000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Qita Al Kirsh, Al-Lith, Red Sea Table 1 Western Indian Ocean Middle East 20.140700 40.09310 20.140700 40.09310 Table 1 NA NA NA Indian Ocean 30.81250 30.8125 0.8379212 10 11 30.25667 30.300 1.0156939 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 6588 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4396286 304.25 4.065706
295 EI0304 CD020 SI066 1.7200000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Fringing reef 1, Al-Lith, Red Sea Table 1 Western Indian Ocean Middle East 20.173200 40.16130 20.173200 40.16130 Table 1 NA NA NA Indian Ocean 30.81250 30.8125 0.8379212 10 11 30.25667 30.300 1.0156939 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 1281 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.0107117 304.25 4.219973
296 EI0305 CD020 SI067 0.1200000 % NA Table 1 2015 Oct-Nov Oct-Nov Materials and Methods Black band disease surveys Whaleshark reef, Al-Lith, Red Sea Table 1 Western Indian Ocean Middle East 20.123000 40.21180 20.123000 40.21180 Table 1 NA NA NA Indian Ocean 30.81250 30.8125 0.8379212 10 11 30.25667 30.300 1.0156939 aut 10 fall NA NA NA 0.15000 22 Material and Methods Black band disease surveys 1716 1 NA Belt 1 25.0000 6.00 150.0000 Materials and Methods Black band disease surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.9178772 304.26 4.367127
297 EI0306 CD016 SI068 3.2200000 % 0.8 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Borendi, New Caledonia Table 1 Western Pacific Melanesia -21.786667 166.46500 -21.786667 166.46500 Materials and Methods Study locations p166 NA NA pigmentation Pacific Ocean 27.09000 27.0900 NA 3 3 26.86433 27.055 0.5132851 aut 9 fall NA NA NA 0.06000 4 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3939285 300.54 0.000000
298 EI0307 CD016 SI068 1.5200000 % 0.44 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Borendi, New Caledonia Table 1 Western Pacific Melanesia -21.786667 166.46500 -21.786667 166.46500 Materials and Methods Study locations p166 NA NA growth anomoly Pacific Ocean 27.09000 27.0900 NA 3 3 26.86433 27.055 0.5132851 aut 9 fall NA NA NA 0.06000 4 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3939285 300.54 0.000000
299 EI0308 CD016 SI068 1.0700000 % 0.47 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Borendi, New Caledonia Table 1 Western Pacific Melanesia -21.786667 166.46500 -21.786667 166.46500 Materials and Methods Study locations p166 NA NA white syndrome Pacific Ocean 27.09000 27.0900 NA 3 3 26.86433 27.055 0.5132851 aut 9 fall NA NA NA 0.06000 4 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3939285 300.54 0.000000
300 EI0309 CD016 SI069 11.1000000 % 2.7 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Kaala, New Caledonia Table 1 Western Pacific Melanesia -20.615000 164.31500 -20.615000 164.31500 Materials and Methods Study locations p166 NA NA pigmentation Pacific Ocean 27.29300 27.2930 NA 3 3 27.29333 27.565 0.5100074 aut 9 fall NA NA NA 0.06000 3 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3778381 300.75 0.000000
301 EI0310 CD016 SI069 3.0100000 % 1.03 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Kaala, New Caledonia Table 1 Western Pacific Melanesia -20.615000 164.31500 -20.615000 164.31500 Materials and Methods Study locations p166 NA NA growth anomoly Pacific Ocean 27.29300 27.2930 NA 3 3 27.29333 27.565 0.5100074 aut 9 fall NA NA NA 0.06000 3 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3778381 300.75 0.000000
302 EI0311 CD016 SI069 0.0000000 % 0 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Kaala, New Caledonia Table 1 Western Pacific Melanesia -20.615000 164.31500 -20.615000 164.31500 Materials and Methods Study locations p166 NA NA white syndrome Pacific Ocean 27.29300 27.2930 NA 3 3 27.29333 27.565 0.5100074 aut 9 fall NA NA NA 0.06000 3 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3778381 300.75 0.000000
303 EI0312 CD016 SI070 0.3100000 % 0.31 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Nepoui, New Caledonia Table 1 Western Pacific Melanesia -21.343333 164.99500 -21.343300 164.91940 Materials and Methods Study locations p166 NA NA coordinates not found in SST dataset - reselected from a nearby coordinate in GoogleMaps Pacific Ocean 26.85500 26.8550 NA 3 3 26.80767 27.035 0.5457484 aut 9 fall NA NA NA 0.06000 2 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5389175 300.34 0.000000
304 EI0313 CD016 SI070 4.1600000 % 2.54 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Nepoui, New Caledonia Table 1 Western Pacific Melanesia -21.343333 164.99500 -21.343300 164.91940 Materials and Methods Study locations p166 NA NA coordinates not found in SST dataset - reselected from a nearby coordinate in GoogleMaps Pacific Ocean 26.85500 26.8550 NA 3 3 26.80767 27.035 0.5457484 aut 9 fall NA NA NA 0.06000 2 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5389175 300.34 0.000000
305 EI0314 CD016 SI070 0.0000000 % 0 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Nepoui, New Caledonia Table 1 Western Pacific Melanesia -21.343333 164.99500 -21.343300 164.91940 Materials and Methods Study locations p166 NA NA coordinates not found in SST dataset - reselected from a nearby coordinate in GoogleMaps Pacific Ocean 26.85500 26.8550 NA 3 3 26.80767 27.035 0.5457484 aut 9 fall NA NA NA 0.06000 2 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5389175 300.34 0.000000
306 EI0315 CD016 SI071 2.0700000 % 0.47 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Thio, New Caledonia Table 1 Western Pacific Melanesia -21.616667 166.25500 -21.616667 166.25500 Materials and Methods Study locations p166 NA NA pigmentation Pacific Ocean 27.09000 27.0900 NA 3 3 26.86433 27.055 0.5132851 aut 9 fall NA NA NA 0.06000 3 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3728485 300.62 0.000000
307 EI0316 CD016 SI071 1.7800000 % 0.6 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Thio, New Caledonia Table 1 Western Pacific Melanesia -21.616667 166.25500 -21.616667 166.25500 Materials and Methods Study locations p166 NA NA growth anomoly Pacific Ocean 27.09000 27.0900 NA 3 3 26.86433 27.055 0.5132851 aut 9 fall NA NA NA 0.06000 3 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3728485 300.62 0.000000
308 EI0317 CD016 SI071 0.0700000 % 7.00E-02 Table 1 2013 Mar Mar Materials and Methods Study locations p166 Thio, New Caledonia Table 1 Western Pacific Melanesia -21.616667 166.25500 -21.616667 166.25500 Materials and Methods Study locations p166 NA NA white syndrome Pacific Ocean 27.09000 27.0900 NA 3 3 26.86433 27.055 0.5132851 aut 9 fall NA NA NA 0.06000 3 Materials and Methods Survey methods p167 NA 1 NA Belt 3 20.0000 1.00 20.0000 Materials and Methods Survey method p167 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3728485 300.62 0.000000
309 EI0318 CD046 SI072 0.0000000 % 0 Discussion 2018 Feb-Mar Feb-Mar Materials and Methods Data collection and construction of ortho-mosaics Cozumel Reefs National Park Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 20.385380 -87.02705 20.385380 -87.02705 GoogleMaps Cozumel Reef National Park, Figure 1 NA NA NA Atlantic Ocean 26.77250 26.7725 0.1590993 2 3 29.06467 29.203 0.4343486 spr 2 winter 0.0000000 NA NA 2.70000 6 Figure 1 NA 1 Sampling area calculated from transect data, but summing sampling area reported in Table 2 = 3.775 Belt 3 30.0000 5.00 150.0000 Materials and Methods Data collection and construction of ortho-mosaics NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6324921 301.91 6.024253
310 EI0319 CD047 SI073 13.0000000 % 5.5 Results Island patterns p5 2008 Feb-Apr Feb-Apr Materials and Methods Survey design p3 Christmas Island Results Island patterns p5 Eastern Indian Ocean Southeast Asia -10.500000 105.66670 -10.500000 105.66670 Materials and Methods Study site p2, GoogleMaps NA NA NA Indian Ocean 28.86633 28.8580 0.2026289 2 4 28.50434 28.798 0.3159690 aut 8 summer NA NA NA 4.50000 10 Materials and Methods Survey design p3 NA 1 Acropora plate corals Belt 3 30.0000 5.00 150.0000 Materials and Methods Survey design p3 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1864319 301.97 0.000000
311 EI0320 CD047 SI074 0.8500000 % 0.63 Results Island patterns p5 2008 Feb-Apr Feb-Apr Materials and Methods Survey design p3 Cocos Islands Results Island patterns p5 Eastern Indian Ocean Southeast Asia -12.200000 96.90000 -12.200000 96.90000 Materials and Methods Study site p2, GoogleMaps NA NA NA Indian Ocean 28.51700 28.5230 0.0103921 2 4 27.58933 27.755 0.6544199 aut 8 summer NA NA NA 4.05000 9 Materials and Methods Survey design p3 NA 1 Acropora plate corals Belt 3 30.0000 5.00 150.0000 Materials and Methods Survey design p3 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 301.49 0.000000
312 EI0321 CD048 SI075 71.0000000 % NA Table 3 2015 Mar Mar Materials and Methods Underwater survey p80 Malvan Marine Sanctuary Figure 1 Western Indian Ocean Central Indian 16.044722 73.46139 16.044722 73.46139 Table 3, GoogleMaps NA NA Site 1 Indian Ocean 28.22300 28.2230 NA 3 3 29.07267 28.993 0.5369502 spr 3 spring NA NA NA 0.30000 1 Figure 1 24 0 Effect sizes are per site, colony number mean per transect Belt 3 50.0000 2.00 100.0000 Materials and Methods Underwater survey p81 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 NA 0.8071289 302.54 0.000000
313 EI0322 CD048 SI076 40.0000000 % NA Table 3 2015 Mar Mar Materials and Methods Underwater survey p80 Malvan Marine Sanctuary Figure 1 Western Indian Ocean Central Indian 16.065000 73.45722 16.065000 73.45722 Table 3, GoogleMaps NA NA Site 2 Indian Ocean 28.22300 28.2230 NA 3 3 29.07267 28.993 0.5369502 spr 3 spring NA NA NA 0.30000 1 Figure 1 10 0 Effect sizes are per site, colony number mean per transect Belt 3 50.0000 2.00 100.0000 Materials and Methods Underwater survey p81 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 NA 0.7456970 302.53 0.000000
314 EI0323 CD049 SI077 2.5405410 % 0.4504505 Figure 4b 2005 Aug-Sep Aug-Sep Materials and Methods Kingman, Line Islands Figure 1 Western Pacific Micronesia 6.410630 -162.42692 6.410630 -162.42692 GoogleMaps Kingman Reef 27.90000 Table 1 Prevalence extracted using MetaDigitise in Rstudio, n = 20 Pacific Ocean 28.88150 28.8815 0.1322292 8 9 28.54300 28.488 0.2226552 aut 8 summer 2.0144760 NA 0.7000 0.80000 10 Materials and Methods Coral Disease NA 1 NA Belt 2 20.0000 2.00 40.0000 Materials and Methods Coral Disease NA 5 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2214203 302.13 0.000000
315 EI0324 CD049 SI078 4.8288290 % 2 Figure 4b 2005 Aug-Sep Aug-Sep Materials and Methods Palmyra, Line Islands Figure 1 Western Pacific Micronesia 5.921290 -162.09040 5.921290 -162.09040 GoogleMaps Palmyra Atoll 27.90000 Table 1 Prevalence extracted using MetaDigitise in Rstudio, n = 20 Pacific Ocean 28.83150 28.8315 0.0586900 8 9 28.64667 28.625 0.1338217 aut 8 summer 8.9442720 NA 0.8000 0.80000 10 Materials and Methods Coral Disease NA 1 NA Belt 2 20.0000 2.00 40.0000 Materials and Methods Coral Disease NA 5 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2749939 302.11 0.000000
316 EI0325 CD049 SI079 6.2522520 % 1.3873874 Figure 4b 2005 Aug-Sep Aug-Sep Materials and Methods Tabuaeran, Line Islands Figure 1 Western Pacific Micronesia 3.878780 -159.31828 3.878780 -159.31828 GoogleMaps Tabuaeran 27.50000 Table 1 Prevalence extracted using MetaDigitise in Rstudio, n = 20 Pacific Ocean 28.29500 28.2950 0.0494973 8 9 28.42767 28.435 0.1641225 aut 8 summer 6.2045850 NA 0.9000 0.80000 10 Materials and Methods Coral Disease NA 1 NA Belt 2 20.0000 2.00 40.0000 Materials and Methods Coral Disease NA 5 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7703476 301.54 2.192828
317 EI0326 CD049 SI080 6.3243240 % 1.4234234 Figure 4b 2005 Aug-Sep Aug-Sep Materials and Methods Kiritimati, Line Islands Figure 1 Western Pacific Micronesia 1.886260 -157.43060 1.965730 -157.46071 GoogleMaps Kiritimati 27.10000 Table 1 Prevalence extracted using MetaDigitise in Rstudio, n = 20 Pacific Ocean 27.45150 27.4515 0.1110155 8 9 27.76967 27.723 0.4219400 aut 8 summer 6.3657430 NA 1.1000 0.80000 10 Materials and Methods Coral Disease NA 1 NA Belt 2 20.0000 2.00 40.0000 Materials and Methods Coral Disease NA 5 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3664398 301.17 0.000000
319 EI0328 CD051 SI082 0.3523810 % 9.05E-02 Figure 2a 2014 Oct-Nov Oct-Nov Materials and Methods Disease surveys Filitheyo Faafu Atoll, Maldives Figure 1 Western Indian Ocean Central Indian 3.214300 73.03688 3.214300 73.03688 GoogleMaps Filitheyo Island, Maldives NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 24 Indian Ocean 28.91500 28.9150 0.1060657 10 11 29.50800 29.433 0.0653841 aut 10 fall 0.4432410 NA NA 1.20000 8 Materials and Methods Disease surveys NA 1 NA Belt 24 25.0000 2.00 50.0000 Materials and Methods Disease surveys NA 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3435669 302.68 0.000000
320 EI0329 CD051 SI083 0.6428571 % 0.14285714 Figure 2a 2014 Oct-Nov Oct-Nov Materials and Methods Disease surveys Adanga Faafu Atoll, Maldives Figure 1 Western Indian Ocean Central Indian 3.134280 73.01831 3.134280 73.01831 GoogleMaps, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 24 Indian Ocean 28.91500 28.9150 0.1060657 10 11 29.50800 29.433 0.0653841 aut 10 fall 0.6998542 NA NA 1.20000 8 Materials and Methods Disease surveys NA 1 NA Belt 24 25.0000 2.00 50.0000 Materials and Methods Disease surveys NA 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3185730 302.67 0.000000
321 EI0330 CD051 SI084 0.6333300 % 0.10952381 Figure 2a 2014 Oct-Nov Oct-Nov Materials and Methods Disease surveys Magoodhoo Faafu Atoll, Maldives Figure 1 Western Indian Ocean Central Indian 3.090470 72.96381 3.090470 72.96381 GoogleMaps Magoodhoo, Maldives NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 24 Indian Ocean 28.92650 28.9265 0.1081873 10 11 29.58967 29.508 0.0728580 aut 10 fall 0.5365549 NA NA 1.20000 8 Materials and Methods Disease surveys NA 1 NA Belt 24 25.0000 2.00 50.0000 Materials and Methods Disease surveys NA 2 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3328552 302.67 0.000000
322 EI0331 CD052 SI085 1.5000000 % 1 Results p151 2011 Oct Oct Methods p151 Barranglompo Spermonde Archipelago, South Sulawesi Methods p151 Coral Triangle & SE Asia Southeast Asia -5.046667 119.33000 -5.046667 119.33000 Methods p151, GoogleMaps NA NA deep site Pacific Ocean 28.75500 28.7550 NA 10 10 29.08200 28.848 0.6656004 spr 4 spring NA NA NA 0.09000 1 Methods p151 NA 1 NA Belt 3 15.0000 2.00 30.0000 Methods p151 NA 5 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1735840 302.57 9.384250
323 EI0332 CD052 SI085 4.6000000 % 6 Results p151 2011 Oct Oct Methods p151 Barranglompo Spermonde Archipelago, South Sulawesi Methods p151 Coral Triangle & SE Asia Southeast Asia -5.046667 119.33000 -5.046667 119.33000 Methods p151, GoogleMaps NA NA shallow site Pacific Ocean 28.75500 28.7550 NA 10 10 29.08200 28.848 0.6656004 spr 4 spring NA NA NA 0.06000 1 Methods p151 NA 1 NA Belt 2 15.0000 2.00 30.0000 Methods p151 NA 5 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1735840 302.57 9.384250
324 EI0333 CD053 SI086 0.1517241 % 3.25E-02 Figure 4a 2011 Jun-Sep Jun-Sep Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.07700 28.9865 0.3758093 6 9 28.62267 28.333 0.5349637 win 12 winter 0.1379310 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2235641 302.56 1.241423
325 EI0334 CD053 SI086 0.7655172 % 0.17230648 Figure 4a 2011 Oct-Nov Oct-Nov Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.62900 29.6290 0.1923326 10 11 28.62267 28.333 0.5349637 spr 4 spring 0.7310345 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3900146 302.56 0.000000
326 EI0335 CD053 SI086 0.3241379 % 4.06E-02 Figure 4a 2012 Dec-Mar Dec-Mar Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.10125 28.8525 0.2945723 12 3 28.99167 28.855 0.2410559 aut 6 summer 0.1724138 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5635605 302.56 0.000000
327 EI0336 CD053 SI086 0.1517241 % 4.71E-02 Figure 4a 2012 Apr-May Apr-May Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.62000 29.6200 0.1555630 4 5 28.99167 28.855 0.2410559 aut 10 fall 0.2000000 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8056946 302.56 0.000000
328 EI0337 CD053 SI086 0.1172414 % 2.60E-02 Figure 4a 2011 Jun-Sep Jun-Sep Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.07700 28.9865 0.3758093 6 9 28.62267 28.333 0.5349637 win 12 winter 0.1103448 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2235641 302.56 1.241423
329 EI0338 CD053 SI086 0.0620690 % 1.63E-02 Figure 4a 2011 Oct-Nov Oct-Nov Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.62900 29.6290 0.1923326 10 11 28.62267 28.333 0.5349637 spr 4 spring 0.0689655 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3900146 302.56 0.000000
330 EI0339 CD053 SI086 0.0137931 % 4.88E-03 Figure 4a 2012 Dec-Mar Dec-Mar Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.10125 28.8525 0.2945723 12 3 28.99167 28.855 0.2410559 aut 6 summer 0.0206897 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5635605 302.56 0.000000
331 EI0340 CD053 SI086 0.0068966 % 1.63E-03 Figure 4a 2012 Apr-May Apr-May Figure 4a Kepulauan Seribu, Indonesia Materials and Methods 2.1 Study Site p106 Coral Triangle & SE Asia Southeast Asia -5.600000 106.55069 -5.600000 106.55069 GoogleMaps Kepulauan Seribu, Indonesia NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 18 Pacific Ocean 29.62000 29.6200 0.1555630 4 5 28.99167 28.855 0.2410559 aut 10 fall 0.0068966 NA NA 0.36000 6 Results p106 NA 1 NA Belt 18 20.0000 1.00 20.0000 Materials and Methods 2.2.2 Coral disease abundance p106 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8056946 302.56 0.000000
332 EI0341 CD054 SI087 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Gabuo PR Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.909560 100.33414 -0.909560 100.33414 Table 1 31.50000 Table 2 BBD, Prevalence extracted using MetaDigitise in Rstudio, n = 3 Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7539291 302.74 0.000000
333 EI0342 CD054 SI088 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Pisang Is Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.997270 100.33865 -0.997270 100.33865 Table 1 31.00000 Table 2 BBD, Prevalence extracted using MetaDigitise in Rstudio, n = 3 Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6014099 302.75 0.000000
334 EI0343 CD054 SI089 0.3088235 % NA Figure 3 2014 May May Materials and Methods p182 Air PR Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.883980 100.21338 -0.883980 100.21338 Table 1 31.30000 Table 2 BBD, Prevalence extracted using MetaDigitise in Rstudio, n = 3 Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1149597 302.73 1.191418
335 EI0344 CD054 SI090 0.4852941 % NA Figure 3 2014 May May Materials and Methods p182 Sipakal PR Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.927640 100.25074 -0.927640 100.25074 Table 1 31.70000 Table 2 BBD, Prevalence extracted using MetaDigitise in Rstudio, n = 3 Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9078674 302.74 1.065695
336 EI0345 CD054 SI091 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Pieh Is Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.877000 100.09838 -0.877000 100.09838 Table 1 30.20000 Table 2 BBD, Prevalence extracted using MetaDigitise in Rstudio, n = 6 Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.36000 1 Materials and Methods p182 NA 1 NA Belt 6 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6943054 302.71 2.407150
337 EI0346 CD054 SI092 0.7352941 % NA Figure 3 2014 May May Materials and Methods p182 Pandan Is Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.952850 100.14035 -0.952850 100.14035 Table 1 31.50000 Table 2 BBD, Prevalence extracted using MetaDigitise in Rstudio, n = 6 Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.36000 1 Materials and Methods p182 NA 1 NA Belt 6 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4353104 302.72 1.067141
338 EI0347 CD054 SI087 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Gabuo PR Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.909560 100.33414 -0.909560 100.33414 Table 1 31.50000 Table 2 WS, Prevalence extracted using MetaDigitise in Rstudio, n = Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7539291 302.74 0.000000
339 EI0348 CD054 SI088 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Pisang Is Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.997270 100.33865 -0.997270 100.33865 Table 1 31.00000 Table 2 WS, Prevalence extracted using MetaDigitise in Rstudio, n = Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6014099 302.75 0.000000
340 EI0349 CD054 SI089 0.3088235 % NA Figure 3 2014 May May Materials and Methods p182 Air PR Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.883980 100.21338 -0.883980 100.21338 Table 1 31.30000 Table 2 WS, Prevalence extracted using MetaDigitise in Rstudio, n = Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1149597 302.73 1.191418
341 EI0350 CD054 SI090 0.3088235 % NA Figure 3 2014 May May Materials and Methods p182 Sipakal PR Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.927640 100.25074 -0.927640 100.25074 Table 1 31.70000 Table 2 WS, Prevalence extracted using MetaDigitise in Rstudio, n = Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.18000 1 Materials and Methods p182 NA 1 NA Belt 3 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9078674 302.74 1.065695
342 EI0351 CD054 SI091 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Pieh Is Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.877000 100.09838 -0.877000 100.09838 Table 1 30.20000 Table 2 WS, Prevalence extracted using MetaDigitise in Rstudio, n = Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.36000 1 Materials and Methods p182 NA 1 NA Belt 6 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6943054 302.71 2.407150
343 EI0352 CD054 SI092 0.0000000 % 0 Figure 3 2014 May May Materials and Methods p182 Pandan Is Padang Shelf Reef Figure 1 Eastern Indian Ocean Southeast Asia -0.952850 100.14035 -0.952850 100.14035 Table 1 31.50000 Table 2 WS, Prevalence extracted using MetaDigitise in Rstudio, n = Indian Ocean 30.03800 30.0380 NA 5 5 29.02767 28.990 0.0420280 aut 11 fall NA NA NA 0.36000 1 Materials and Methods p182 NA 1 NA Belt 6 30.0000 2.00 60.0000 Materials and Methods p182 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4353104 302.72 1.067141
344 EI0353 CD055 SI093 11.0000000 % 0.9 Table 1 1998 Mar Mar Materials and Methods Abundance and distribution of diseased corals p63 St Lucia, Caribbean Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 13.869460 -61.08087 13.869460 -61.08087 GoogleMaps Anse Chastanet Beach NA NA NA Atlantic Ocean 27.07000 27.0700 NA 3 3 28.82533 28.648 0.5912922 spr 3 spring NA NA NA 5.40000 3 Materials and Methods Abundance and distribution of diseased corals p63 3081 1 NA Belt 27 40.0000 5.00 200.0000 Materials and Methods Abundance and distribution of diseased corals p63 NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 In paper disease referred to as “plague” but described to leave behind white skeleton, so classified here as white syndrome (white plague) 0.3432236 301.59 1.045712
345 EI0354 CD056 SI094 2.2800000 % 0.39 Results Disease types and prevalence p79 2010 Jan Jan Figure 1 Ningaloo Reef, Western Australia Figure 1 Eastern Indian Ocean Australia -22.495000 114.07500 -22.675770 113.65518 Materials and Methods Study area p77 averaged NA NA coordinates not found in SST database - reselected from GoogleMaps Indian Ocean 25.71300 25.7130 NA 1 1 27.20367 27.078 1.3778048 sum 7 summer NA NA NA 1.20000 10 Figure 1 NA 1 NA Belt 30 20.0000 2.00 40.0000 Materials and Methods Study design p77, Figure 1 NA 7 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “non-black cyanobacterial bands (OtCy)” labeled here as Cyano 3.1900330 300.54 1.268564
346 EI0355 CD056 SI095 0.8000000 % 0.2 Figure 1a 2009 May May Figure 1 Bill’s Bay, Western Australia Figure 1 Eastern Indian Ocean Australia -23.140000 113.76000 -23.140000 113.76000 Materials and Methods Study area p77 NA NA NA Indian Ocean 25.68000 25.6800 NA 5 5 24.98033 25.018 0.7322267 aut 11 fall NA NA NA 1.56000 13 Figure 1 NA 1 NA Belt 39 20.0000 2.00 40.0000 Materials and Methods Study design p77, Figure 1 NA 7 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “non-black cyanobacterial bands (OtCy)” labeled here as Cyano 2.9400177 299.93 1.131433
347 EI0356 CD056 SI095 4.1000000 % 0.8 Figure 1a 2010 Jan Jan Figure 1 Bill’s Bay, Western Australia Figure 1 Eastern Indian Ocean Australia -23.140000 113.76000 -23.140000 113.76000 Materials and Methods Study area p77 NA NA NA Indian Ocean 25.01800 25.0180 NA 1 1 26.55933 26.440 1.3878540 sum 7 summer NA NA NA 0.60000 5 Figure 1 NA 1 NA Belt 15 20.0000 2.00 40.0000 Materials and Methods Study design p77, Figure 1 NA 7 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “non-black cyanobacterial bands (OtCy)” labeled here as Cyano 2.6057434 299.93 1.131433
348 EI0357 CD057 SI096 0.1552000 % 4.30E-03 Figure 4 1996 0 0 Figure 4 Florida Keys National Marine Sanctuary Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.703320 -80.96646 24.703320 -80.96646 GoogleMaps West Turtle Shoal Reef, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 160 Atlantic Ocean 29.35300 29.3530 NA 7 7 29.16267 29.353 0.5240951 sum 7 summer NA NA NA 6.40000 40 Figure 1 NA 1 NA Belt 160 20.0000 2.00 40.0000 Materials and Methods Static and dynamic trends in the geographic distribution of coral disease p2-3 NA 10 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 “white plague” characterized as WS, “yellow blotch” characterized as YBD, “neoplasia” characterized as GA 1.3979034 302.88 2.828563
349 EI0358 CD057 SI096 0.6528000 % 4.55E-03 Figure 4 1997 0 0 Figure 4 Florida Keys National Marine Sanctuary Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.703320 -80.96646 24.703320 -80.96646 GoogleMaps West Turtle Shoal Reef, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 160 Atlantic Ocean 30.13000 30.1300 NA 7 7 29.83667 30.130 0.9399775 sum 7 summer NA NA NA 6.40000 40 Figure 1 NA 1 NA Belt 160 20.0000 2.00 40.0000 Materials and Methods Static and dynamic trends in the geographic distribution of coral disease p2-3 NA 10 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 “white plague” characterized as WS, “yellow blotch” characterized as YBD, “neoplasia” characterized as GA 1.2671509 302.88 0.000000
350 EI0359 CD057 SI096 0.8016000 % 4.93E-03 Figure 4 1998 0 0 Figure 4 Florida Keys National Marine Sanctuary Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.703320 -80.96646 24.703320 -80.96646 GoogleMaps West Turtle Shoal Reef, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 160 Atlantic Ocean 30.21500 30.2150 NA 7 7 29.98100 30.215 0.6913671 sum 7 summer NA NA NA 6.40000 40 Figure 1 NA 1 NA Belt 160 20.0000 2.00 40.0000 Materials and Methods Static and dynamic trends in the geographic distribution of coral disease p2-3 NA 10 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 “white plague” characterized as WS, “yellow blotch” characterized as YBD, “neoplasia” characterized as GA 0.3574982 302.88 7.442838
351 EI0360 CD058 SI097 65.1000000 % NA Results 3.2 Disease abundance 2002 Jun-Aug Jun-Aug Results 3.2 Disease abundance Florida Keys National Marine Sanctuary Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.703320 -80.96646 24.703320 -80.96646 GoogleMaps West Turtle Shoal Reef, Figure 1 NA NA NA Atlantic Ocean 29.24833 29.4950 0.8570490 6 8 29.24833 29.495 0.8570490 sum 6 summer NA NA NA 0.28000 7 Figure 1 238 1 Admiral Patch Reef only for disease progression, not prevalence Belt 7 20.0000 2.00 40.0000 Methods NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other diseases found, but prevalence only reported for DSS 2.3356934 302.88 1.012853
352 EI0361 CD058 SI097 66.1000000 % NA Results 3.2 Disease abundance 2003 Jun-Aug Jun-Aug Results 3.2 Disease abundance Florida Keys National Marine Sanctuary Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.703320 -80.96646 24.703320 -80.96646 GoogleMaps West Turtle Shoal Reef, Figure 1 NA NA NA Atlantic Ocean 29.34467 29.4180 0.5834666 6 8 29.34467 29.418 0.5834666 sum 6 summer NA NA NA 0.28000 7 Figure 1 157 1 Admiral Patch Reef only for disease progression, not prevalence Belt 7 20.0000 2.00 40.0000 Methods NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other diseases found, but prevalence only reported for DSS 1.5714416 302.88 0.000000
353 EI0362 CD058 SI097 82.5000000 % NA Results 3.2 Disease abundance 2004 Jun-Aug Jun-Aug Results 3.2 Disease abundance Florida Keys National Marine Sanctuary Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.703320 -80.96646 24.703320 -80.96646 GoogleMaps West Turtle Shoal Reef, Figure 1 NA NA NA Atlantic Ocean 29.64467 29.9630 0.6671268 6 8 29.64467 29.963 0.6671268 sum 6 summer NA NA NA 0.28000 7 Figure 1 299 1 Admiral Patch Reef only for disease progression, not prevalence Belt 7 20.0000 2.00 40.0000 Methods NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other diseases found, but prevalence only reported for DSS 0.3135834 302.88 0.000000
354 EI0363 CD059 SI098 15.4901960 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Kavaratti Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 10.568190 72.64148 10.568190 72.64148 GoogleMaps Kavaratti Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.83630 27.9825 0.6952444 3 12 28.33100 28.145 0.6251107 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2378387 303.15 2.304249
355 EI0364 CD059 SI099 11.3071900 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Agatti Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 10.869020 72.19553 10.869020 72.19553 GoogleMaps Agatti Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.83630 27.9825 0.6952444 3 12 28.33100 28.145 0.6251107 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3021469 303.10 2.082838
356 EI0365 CD059 SI100 19.2156860 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Thinnakara Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 10.947780 72.31874 10.947780 72.31874 GoogleMaps Thinnakara Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.83630 27.9825 0.6952444 3 12 28.33100 28.145 0.6251107 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2114258 303.10 3.117147
357 EI0366 CD059 SI101 36.8627450 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Minicoy Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 8.275070 73.04914 8.275070 73.04914 GoogleMaps Minicoy Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.80950 27.9290 0.7588506 3 12 28.24267 28.043 0.5551184 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2614441 303.19 3.338541
358 EI0367 CD059 SI102 10.9803920 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Kalpeni Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 10.078070 73.63037 10.078070 73.63037 GoogleMaps Kalpeni Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.80800 27.8615 0.7676206 3 12 28.24267 28.043 0.6846939 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1664200 303.16 2.091409
359 EI0368 CD059 SI103 7.0588240 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Chethlath Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 11.745800 72.69655 11.745800 72.69655 GoogleMaps Chetlat Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.83270 27.9905 0.7024968 3 12 28.36700 28.193 0.6828340 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3810654 303.10 1.090003
360 EI0369 CD059 SI104 33.5947710 % NA Figure 3 2011 Mar-Apr, Nov-Dec Mar-Apr, Nov-Dec Materials and Methods p2 Bangaram Island, Lakshadweep, Indian Ocean Material and Methods p2 Western Indian Ocean Central Indian 10.937070 72.28794 10.937070 72.28794 GoogleMaps Bangaram Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Indian Ocean 28.83630 27.9825 0.6952444 3 12 28.33100 28.145 0.6251107 multi 3 spring NA NA NA 0.15000 1 Materials and Methods p2 NA 1 NA Line 5 30.0000 1.00 30.0000 Materials and Methods p2 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2514343 303.10 3.122854
361 EI0370 CD060 SI105 16.7000000 % 2.1 Table 1 1995 Aug-Sep Aug-Sep Materials and Methods p148 Northern Florida Keys Materials and Methods p148 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.909940 -80.75177 24.909940 -80.75177 GoogleMaps Florida Keys NA NA Lat and Lon random point selected in Florida Keys - no specific location given in paper Atlantic Ocean 29.71400 29.7140 0.1046523 8 9 29.33333 29.615 0.5096653 aut 8 summer NA NA NA 8.47800 7 Materials and Methods p3 NA 0 Site Num = reef number Circle 27 20.0000 NA 314.0000 Materials and Methods p2-3 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white plague II” categorized as WS 1.7842865 302.85 8.547099
362 EI0371 CD060 SI105 3.2000000 % 1.5 Table 1 1998 Aug Aug Materials and Methods p148 Northern Florida Keys Materials and Methods p148 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.909940 -80.75177 24.909940 -80.75177 GoogleMaps Florida Keys NA NA Lat and Lon random point selected in Florida Keys - no specific location given in paper Atlantic Ocean 30.52500 30.5250 NA 8 8 29.98100 30.215 0.6913671 sum 8 summer NA NA NA 6.28000 6 Materials and Methods p3 NA 0 Site Num = reef number Circle 20 20.0000 NA 314.0000 Materials and Methods p2-3 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white plague II” categorized as WS 0.9785767 302.85 17.969947
363 EI0372 CD060 SI105 0.0000000 % 0 Table 1 2002 Aug Aug Materials and Methods p148 Northern Florida Keys Materials and Methods p148 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.909940 -80.75177 24.909940 -80.75177 GoogleMaps Florida Keys NA NA Lat and Lon random point selected in Florida Keys - no specific location given in paper Atlantic Ocean 29.95500 29.9550 NA 8 8 29.24833 29.495 0.8570490 sum 8 summer NA NA NA 8.79200 9 Materials and Methods p3 NA 0 Site Num = reef number Circle 28 20.0000 NA 314.0000 Materials and Methods p2-3 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white plague II” categorized as WS 1.2357178 302.85 8.524272
364 EI0373 CD061 SI106 23.7383200 % 5.099071 Figure 2 1999 Jun-Aug Jun-Aug Materials and Methods Eleuthera, Bahamas Figure 2 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.969960 -76.17788 24.969960 -76.17788 GoogleMaps Eleuthera NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Atlantic Ocean 28.51367 28.4830 0.9363775 6 8 28.51367 28.483 0.9363775 sum 6 summer 11.4018700 NA NA 0.11250 5 Figure 1 NA 1 NA Belt 5 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1271667 302.36 9.592828
365 EI0374 CD061 SI107 11.9626200 % 4.681114 Figure 2 1999 Jun-Aug Jun-Aug Materials and Methods Cat Island, Bahamas Figure 2 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.327070 -75.44407 24.327070 -75.44407 GoogleMaps Cat Island NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5 Atlantic Ocean 28.43133 28.4080 0.9352188 6 8 28.43133 28.408 0.9352188 sum 6 summer 10.4672900 NA NA 0.11250 5 Figure 1 NA 1 NA Belt 5 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5014648 302.03 4.885679
366 EI0375 CD061 SI108 4.1284400 % 0.9948948 Figure 9 1999 Jun-Aug Jun-Aug Materials and Methods Little Cayman, Cayman Islands Figure 9 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.693290 -80.03727 19.693290 -80.03727 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 15 Atlantic Ocean 29.18267 29.0280 0.5903948 6 8 29.18267 29.028 0.5903948 sum 6 summer 3.8532110 NA NA 0.33750 15 Figure 1 NA 1 NA Belt 15 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7603378 302.62 3.447131
367 EI0376 CD061 SI109 3.7614680 % 0.9345409 Figure 9 1999 Jun-Aug Jun-Aug Materials and Methods Grand Cayman, Cayman Islands Figure 9 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.326950 -81.24179 19.326950 -81.24179 GoogleMaps Grand Cayman NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 17 Atlantic Ocean 29.04300 28.8880 0.5831597 6 8 29.04300 28.888 0.5831597 sum 6 summer 3.8532110 NA NA 0.38250 17 Figure 1 NA 1 NA Belt 17 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7957153 302.47 5.154274
368 EI0377 CD061 SI110 2.4770640 % 0.3481407 Figure 9 1999 Jun-Aug Jun-Aug Materials and Methods Turks Bank, Turks and Caicos Islands Figure 9 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.419630 -71.18804 21.419630 -71.18804 GoogleMaps Turks, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 10 Atlantic Ocean 28.20200 27.9230 0.6177162 6 8 28.20200 27.923 0.6177162 sum 6 summer 1.1009170 NA NA 0.22500 10 Figure 1 NA 1 NA Belt 10 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0950165 301.74 0.000000
369 EI0378 CD061 SI111 5.4128440 % 0.940367 Figure 9 1999 Jun-Aug Jun-Aug Materials and Methods Caicos Bank, Turks and Caicos Islands Figure 9 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.575050 -71.91941 21.575050 -71.91941 GoogleMaps Caicos, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 16 Atlantic Ocean 28.20200 27.9230 0.6177162 6 8 28.20200 27.923 0.6177162 sum 6 summer 3.7614680 NA NA 0.36000 16 Figure 1 NA 1 NA Belt 16 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0417786 301.83 3.948539
370 EI0379 CD061 SI112 12.1100920 % 3.5030978 Figure 9 1999 Jun-Aug Jun-Aug Materials and Methods Mouchoir Bank, Turks and Caicos Islands Figure 9 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.161110 -70.77300 21.161110 -70.77300 GoogleMaps, random guess, Figure 1 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 2 Atlantic Ocean 28.16533 27.8930 0.6201024 6 8 28.16533 27.893 0.6201024 sum 6 summer 4.9541280 NA NA 0.04500 2 Figure 1 NA 1 NA Belt 2 15.0000 1.50 22.5000 Materials and Methods NA 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8568115 301.69 0.000000
371 EI0380 CD062 SI113 7.6635510 % 0.8411215 Figure 3 2004 Dec-Feb Dec-Feb Introduction Plate Ledge, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 1.4568652 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
372 EI0381 CD062 SI113 11.7757010 % 2.8037383 Figure 3 2004 Dec-Feb Dec-Feb Introduction Coral Cascades, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 4.8562172 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
373 EI0382 CD062 SI113 5.8878500 % 0.5607477 Figure 3 2004 Dec-Feb Dec-Feb Introduction 2nd Point, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 0.9712434 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
374 EI0383 CD062 SI113 6.3551400 % 3.4579439 Figure 3 2004 Dec-Feb Dec-Feb Introduction 4th Point, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 5.9893346 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
375 EI0384 CD062 SI113 4.9532710 % 1.3084112 Figure 3 2004 Dec-Feb Dec-Feb Introduction Pams Point, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 2.2662347 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
376 EI0385 CD062 SI113 18.6915890 % 6.5420561 Figure 3 2004 Dec-Feb Dec-Feb Introduction Coral Canyons, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 11.3311735 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
377 EI0386 CD062 SI113 11.5887850 % 1.2149533 Figure 3 2004 Dec-Feb Dec-Feb Introduction Selinas Gutter, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 2.1043608 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
378 EI0387 CD062 SI113 5.2336450 % 1.0280374 Figure 3 2004 Dec-Feb Dec-Feb Introduction Plateau, Heron Reef Figure 3 Western Pacific Australia -23.450960 151.97031 -23.450960 151.97031 GoogleMaps Heron Reef NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 3 Pacific Ocean 27.09700 27.1880 0.6086233 12 2 27.09700 27.188 0.6086233 aut 6 summer 1.7806130 NA NA 0.12000 1 Figure 1 NA 1 NA Belt 3 40.0000 1.00 40.0000 Materials and Methods Spatial patterns of Acroporid white syndrome NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8010864 300.26 1.168559
379 EI0388 CD063 SI114 41.6100000 % 3.93 Results 3.2 Coral cover and disease 2016 Jul Jul Materials and Methods 2.1 Study area Genting Island, Karimunjawa Archipelago Materials and Methods 2.1 Study area Coral Triangle & SE Asia Southeast Asia -5.845328 110.60047 -5.845328 110.60047 Materials and Methods 2.1 Study area, GoogleMaps NA NA NA Pacific Ocean 29.43000 29.4300 NA 7 7 29.04967 29.048 0.5375023 win 1 winter NA NA NA 0.15000 1 Materials and Methods 2.1 Study area 485 1 NA Belt 3 25.0000 2.00 50.0000 Materials and Methods 2.3 Live coral cover and disease prevalence NA 5 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2800064 302.74 4.997125
380 EI0389 CD063 SI115 15.9200000 % 5.04 Results 3.2 Coral cover and disease 2016 Jul Jul Materials and Methods 2.1 Study area Sambangan Island, Karimunjawa Archipelago Materials and Methods 2.1 Study area Coral Triangle & SE Asia Southeast Asia -5.842778 110.58383 -5.842778 110.58383 Materials and Methods 2.1 Study area, GoogleMaps NA NA Why is the latitude so off? - assumed typo, fixed to be -5.84278 (from -45.84278) Pacific Ocean 29.43000 29.4300 NA 7 7 29.04967 29.048 0.5375023 win 1 winter NA NA NA 0.15000 1 Materials and Methods 2.1 Study area 453 1 NA Belt 3 25.0000 2.00 50.0000 Materials and Methods 2.3 Live coral cover and disease prevalence NA 5 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3307190 302.73 5.972867
381 EI0390 CD063 SI116 6.8900000 % 1 Results 3.2 Coral cover and disease 2016 Jul Jul Materials and Methods 2.1 Study area Seruni Island, Karimunjawa Archipelago Materials and Methods 2.1 Study area Coral Triangle & SE Asia Southeast Asia -5.845311 110.60031 -5.845311 110.60031 Materials and Methods 2.1 Study area, GoogleMaps NA NA NA Pacific Ocean 29.43000 29.4300 NA 7 7 29.04967 29.048 0.5375023 win 1 winter NA NA NA 0.15000 1 Materials and Methods 2.1 Study area 317 1 NA Belt 3 25.0000 2.00 50.0000 Materials and Methods 2.3 Live coral cover and disease prevalence NA 4 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2800064 302.74 4.997125
382 EI0391 CD065 SI117 8.7000000 % 2.8 Discussion 2010 Dec Dec Material and Methods Field surveys and progression rate Reunion Island, West Indian Ocean Figure 1 Western Indian Ocean West Indian -21.131505 55.25823 -21.131505 55.25823 Table 1, Averaged across four sites NA NA NA Indian Ocean 26.98500 26.9850 NA 12 12 27.62333 27.860 0.5589353 sum 6 summer NA NA NA 0.88000 4 Material and Methods Field surveys and progression rate 5363 1 4 stations, 3 sites, but use 4 because that’s more relative to the transect number; coral number aggregated from whole study,Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Material and Methods Field surveys and progression rate Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1057129 300.88 0.000000
383 EI0392 CD065 SI117 1.4000000 % 1 Discussion 2011 Oct Oct Material and Methods Field surveys and progression rate Reunion Island, West Indian Ocean Figure 1 Western Indian Ocean West Indian -21.131505 55.25823 -21.131505 55.25823 Table 1, Averaged across four sites NA NA NA Indian Ocean 24.18800 24.1880 NA 10 10 27.54467 27.473 0.7450898 spr 4 spring NA NA NA 0.88000 4 Material and Methods Field surveys and progression rate 5363 1 4 stations, 3 sites, but use 4 because that’s more relative to the transect number; coral number aggregated from whole study, Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Material and Methods Field surveys and progression rate Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2917938 300.88 2.098552
384 EI0393 CD065 SI117 7.7000000 % 2.5 Discussion 2012 Jan Jan Material and Methods Field surveys and progression rate Reunion Island, West Indian Ocean Figure 1 Western Indian Ocean West Indian -21.131505 55.25823 -21.131505 55.25823 Table 1, Averaged across four sites NA NA NA Indian Ocean 27.47300 27.4730 NA 1 1 27.39800 27.518 0.7472615 sum 7 summer NA NA NA 0.88000 4 Material and Methods Field surveys and progression rate 5363 1 4 stations, 3 sites, but use 4 because that’s more relative to the transect number; coral number aggregated from whole study,Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Material and Methods Field surveys and progression rate Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2842865 300.88 2.098552
385 EI0394 CD066 SI118 6.8000000 % NA Table 1 2010 Sep Sep Table 1 Reunion Island, West Indian Ocean Table 1 Western Indian Ocean West Indian -21.120000 55.25000 -21.120000 55.25000 Materials and Methods Disease surveys p250 NA NA NA Indian Ocean 23.66000 23.6600 NA 9 9 27.62333 27.860 0.5589353 spr 3 spring 6.7000000 NA NA 0.88000 4 Table S1 23562 1 coral_n aggregated over all years surveyed, Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Materials and Methods Disease surveys p250 Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 7 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 NA 0.1935730 300.88 0.000000
386 EI0395 CD066 SI118 7.2000000 % NA Table 1 2010 Dec Dec Table 1 Reunion Island, West Indian Ocean Table 1 Western Indian Ocean West Indian -21.120000 55.25000 -21.120000 55.25000 Materials and Methods Disease surveys p250 NA NA NA Indian Ocean 26.98500 26.9850 NA 12 12 27.62333 27.860 0.5589353 sum 6 summer 6.4000000 NA NA 0.88000 4 Table S1 23562 1 coral_n aggregated over all years surveyed, Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Materials and Methods Disease surveys p250 Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 7 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 NA 0.1057129 300.88 0.000000
387 EI0396 CD066 SI118 8.3000000 % NA Table 1 2011 Oct Oct Table 1 Reunion Island, West Indian Ocean Table 1 Western Indian Ocean West Indian -21.120000 55.25000 -21.120000 55.25000 Materials and Methods Disease surveys p250 NA NA NA Indian Ocean 24.18800 24.1880 NA 10 10 27.54467 27.473 0.7450898 spr 4 spring 7.1000000 NA NA 0.88000 4 Table S1 23562 1 coral_n aggregated over all years surveyed, Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Materials and Methods Disease surveys p250 Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 7 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 NA 0.2917938 300.88 2.098552
388 EI0397 CD066 SI118 7.8000000 % NA Table 1 2012 Jan Jan Table 1 Reunion Island, West Indian Ocean Table 1 Western Indian Ocean West Indian -21.120000 55.25000 -21.120000 55.25000 Materials and Methods Disease surveys p250 NA NA NA Indian Ocean 27.47300 27.4730 NA 1 1 27.39800 27.518 0.7472615 sum 7 summer 6.2000000 NA NA 0.88000 4 Table S1 23562 1 coral_n aggregated over all years surveyed, Sample area pooled to account for total area of all transect types Belt 8 15.0000 2.00 30.0000 Materials and Methods Disease surveys p250 Two different transect sizes utilized at reef flats vs reef slope, but both included in this disease prevalence, pooled transect number, averaged transect length and plot area 7 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 NA 0.2842865 300.88 2.098552
389 EI0398 CD066 SI119 3.9000000 % NA Table 1 2011 Feb Feb Table 1 Sodwana Bay, South Africa Table 1 Western Indian Ocean West Indian -27.310000 32.41000 -27.310130 32.76050 Materials and Methods Disease surveys p250 NA NA coordinates not found in SST dataset - reselected from GoogleMaps Indian Ocean 27.19000 27.1900 NA 2 2 26.60433 26.938 0.7642355 sum 8 summer 3.6000000 NA NA 0.70000 7 Table S1 17140 1 coral_n aggregated over all years surveyed Belt 5 10.0000 2.00 20.0000 Materials and Methods Disease surveys p250 NA 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 includes bleaching (see Table 2) 1.6007233 300.18 1.138574
390 EI0399 CD066 SI119 1.9000000 % NA Table 1 2011 Jul Jul Table 1 Sodwana Bay, South Africa Table 1 Western Indian Ocean West Indian -27.310000 32.41000 -27.310130 32.76050 Materials and Methods Disease surveys p250 NA NA coordinates not found in SST dataset - reselected from GoogleMaps Indian Ocean 22.72800 22.7280 NA 7 7 26.60433 26.938 0.7642355 win 1 winter 1.2000000 NA NA 0.70000 7 Table S1 17140 1 coral_n aggregated over all years surveyed Belt 5 10.0000 2.00 20.0000 Materials and Methods Disease surveys p250 NA 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 includes bleaching (see Table 2) 0.7196350 300.18 1.138574
391 EI0400 CD066 SI119 4.1000000 % NA Table 1 2012 Feb Feb Table 1 Sodwana Bay, South Africa Table 1 Western Indian Ocean West Indian -27.310000 32.41000 -27.310130 32.76050 Materials and Methods Disease surveys p250 NA NA coordinates not found in SST dataset - reselected from GoogleMaps Indian Ocean 27.14500 27.1450 NA 2 2 26.80533 27.058 0.7276789 sum 8 summer 2.0000000 NA NA 0.70000 7 Table S1 17140 1 coral_n aggregated over all years surveyed Belt 5 10.0000 2.00 20.0000 Materials and Methods Disease surveys p250 NA 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 includes bleaching (see Table 2) 0.4578552 300.18 0.000000
392 EI0401 CD066 SI119 5.7000000 % NA Table 1 2012 Jun Jun Table 1 Sodwana Bay, South Africa Table 1 Western Indian Ocean West Indian -27.310000 32.41000 -27.310130 32.76050 Materials and Methods Disease surveys p250 NA NA coordinates not found in SST dataset - reselected from GoogleMaps Indian Ocean 23.80500 23.8050 NA 6 6 26.80533 27.058 0.7276789 win 12 winter 3.2000000 NA NA 0.70000 7 Table S1 17140 1 coral_n aggregated over all years surveyed Belt 5 10.0000 2.00 20.0000 Materials and Methods Disease surveys p250 NA 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 includes bleaching (see Table 2) 0.6893005 300.18 0.000000
393 EI0402 CD066 SI120 2.3000000 % NA Table 1 2011 Aug Aug Table 1 Mayotte, West Indian Ocean Table 1 Western Indian Ocean West Indian -12.820000 45.17000 -12.820000 45.17000 Materials and Methods Disease surveys p250 NA NA NA Indian Ocean 25.92800 25.9280 NA 8 8 29.06267 28.855 0.2288364 win 2 winter 3.4000000 NA NA 0.80000 8 Table S1 19426 1 coral_n aggregated over all years surveyed Belt 5 10.0000 2.00 20.0000 Materials and Methods Disease surveys p250 NA 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 NA 0.5371399 302.40 0.000000
394 EI0403 CD066 SI120 3.1000000 % NA Table 1 2012 Mar Mar Table 1 Mayotte, West Indian Ocean Table 1 Western Indian Ocean West Indian -12.820000 45.17000 -12.820000 45.17000 Materials and Methods Disease surveys p250 NA NA NA Indian Ocean 29.25000 29.2500 NA 3 3 28.79367 28.765 0.3608547 aut 9 fall 2.3000000 NA NA 0.80000 8 Table S1 19426 1 coral_n aggregated over all years surveyed Belt 5 10.0000 2.00 20.0000 Materials and Methods Disease surveys p250 NA 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 NA 0.2932205 302.40 0.000000
395 EI0404 CD067 SI121 0.7000000 % 0.8 Results p4 2009 Jan-Apr Jan-Apr Table 2 La Parguera Natural Reserve, Puerto Rico Table 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.924525 -67.02578 17.924525 -67.02578 Table 1 averaged, GoogleMaps NA Figure 4 SST data extracted using MetaDigitise in Rstudio, n = 1440? Atlantic Ocean 26.14325 25.9015 0.3051594 1 4 28.49167 28.575 0.3867924 spr 1 winter NA NA NA 0.10000 6 Table 1 NA 0 NA Belt 5 10.0000 2.00 20.0000 Materials and Methods 2.1 Temporal and spatial variability of CYBD Incidence in M Faveolata p2 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2903595 301.93 0.000000
396 EI0405 CD067 SI121 1.5000000 % 1.1 Results p4 2009 Jun-Sep Jun-Sep Table 2 La Parguera Natural Reserve, Puerto Rico Table 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.924525 -67.02578 17.924525 -67.02578 Table 1 averaged, GoogleMaps NA Figure 4 SST data extracted using MetaDigitise in Rstudio, n = 1464? Atlantic Ocean 28.67375 28.7025 0.4820334 6 9 28.49167 28.575 0.3867924 sum 6 summer NA NA NA 0.10000 6 Table 1 NA 0 NA Belt 5 10.0000 2.00 20.0000 Materials and Methods 2.1 Temporal and spatial variability of CYBD Incidence in M Faveolata p2 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
397 EI0406 CD068 SI122 1.0144930 % 4.1304348 Figure 3 2008 Dec-Feb Dec-Feb Figure 3 Pelotas, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 14.3082460 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4799805 301.94 1.035709
398 EI0407 CD068 SI122 1.0144930 % 4.1304348 Figure 3 2008 Jun-Aug Jun-Aug Figure 3 Pelotas, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 14.3082460 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.6699829 301.94 0.000000
399 EI0408 CD068 SI122 1.2318840 % 4.1304348 Figure 3 2009 Dec-Feb Dec-Feb Figure 3 Pelotas, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 14.3082460 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2275162 301.94 0.000000
400 EI0409 CD068 SI122 3.0434780 % 3.5507246 Figure 3 2009 Jun-Aug Jun-Aug Figure 3 Pelotas, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 12.3000710 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3103561 301.94 1.035709
401 EI0410 CD068 SI123 8.0434780 % 3.4782609 Figure 3 2008 Dec-Feb Dec-Feb Figure 3 Enrique, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 12.0490490 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4671326 301.93 0.000000
402 EI0411 CD068 SI123 7.0289860 % 2.6811594 Figure 3 2008 Jun-Aug Jun-Aug Figure 3 Enrique, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 9.2878090 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.7207184 301.93 0.000000
403 EI0412 CD068 SI123 15.0000000 % 7.3913043 Figure 3 2009 Dec-Feb Dec-Feb Figure 3 Enrique, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 25.6042290 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2192841 301.93 0.000000
404 EI0413 CD068 SI123 13.9855070 % 7.1014493 Figure 3 2009 Jun-Aug Jun-Aug Figure 3 Enrique, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 24.6001420 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
405 EI0414 CD068 SI124 15.0000000 % 3.2608696 Figure 3 2008 Dec-Feb Dec-Feb Figure 3 Media Luna, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 11.2959840 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4671326 301.93 0.000000
406 EI0415 CD068 SI124 16.9565220 % 3.6956522 Figure 3 2008 Jun-Aug Jun-Aug Figure 3 Media Luna, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 12.8021150 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.7207184 301.93 0.000000
407 EI0416 CD068 SI124 15.9420290 % 3.7681159 Figure 3 2009 Dec-Feb Dec-Feb Figure 3 Media Luna, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 13.0531370 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2192841 301.93 0.000000
408 EI0417 CD068 SI124 18.9855070 % 3.5507246 Figure 3 2009 Jun-Aug Jun-Aug Figure 3 Media Luna, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 12.3000710 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
409 EI0418 CD068 SI125 32.9710140 % 3.115942 Figure 3 2008 Dec-Feb Dec-Feb Figure 3 Turrumote, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 10.7939400 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4671326 301.93 0.000000
410 EI0419 CD068 SI125 29.9275360 % 6.0869565 Figure 3 2008 Jun-Aug Jun-Aug Figure 3 Turrumote, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 21.0858360 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.7207184 301.93 0.000000
411 EI0420 CD068 SI125 37.1739130 % 6.0869565 Figure 3 2009 Dec-Feb Dec-Feb Figure 3 Turrumote, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 21.0858360 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2192841 301.93 0.000000
412 EI0421 CD068 SI125 36.8840580 % 6.7391304 Figure 3 2009 Jun-Aug Jun-Aug Figure 3 Turrumote, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 23.3450330 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
413 EI0422 CD068 SI126 3.5507250 % 2.0289855 Figure 3 2008 Dec-Feb Dec-Feb Figure 3 Buoy, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.47533 26.2830 0.5588973 12 2 28.52867 28.443 0.4624894 aut 12 winter 7.0286120 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4467773 301.90 0.000000
414 EI0423 CD068 SI126 1.0144930 % 1.3043478 Figure 3 2008 Jun-Aug Jun-Aug Figure 3 Buoy, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.52867 28.4430 0.4624894 6 8 28.52867 28.443 0.4624894 sum 6 summer 4.5183930 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.6807251 301.90 0.000000
415 EI0424 CD068 SI126 2.5362320 % 1.6666667 Figure 3 2009 Dec-Feb Dec-Feb Figure 3 Buoy, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.32600 27.1650 0.5902057 12 2 28.51334 28.600 0.3873415 aut 12 winter 5.7735030 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.1971588 301.90 0.000000
416 EI0425 CD068 SI126 9.2028990 % 1.0144928 Figure 3 2009 Jun-Aug Jun-Aug Figure 3 Buoy, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.51334 28.6000 0.3873415 6 8 28.51334 28.600 0.3873415 sum 6 summer 3.5143060 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3878632 301.90 0.000000
417 EI0426 CD068 SI127 4.0579710 % 5.3623188 Figure 3 2008 Dec-Feb Dec-Feb Figure 3 Weinberg, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.47533 26.2830 0.5588973 12 2 28.52867 28.443 0.4624894 aut 12 winter 18.5756170 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4467773 301.90 0.000000
418 EI0427 CD068 SI127 0.0000000 % 0 Figure 3 2008 Jun-Aug Jun-Aug Figure 3 Weinberg, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.52867 28.4430 0.4624894 6 8 28.52867 28.443 0.4624894 sum 6 summer 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.6807251 301.90 0.000000
419 EI0428 CD068 SI127 2.1014490 % 2.8985507 Figure 3 2009 Dec-Feb Dec-Feb Figure 3 Weinberg, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.32600 27.1650 0.5902057 12 2 28.51334 28.600 0.3873415 aut 12 winter 10.0408740 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.1971588 301.90 0.000000
420 EI0429 CD068 SI127 7.6811590 % 0.7246377 Figure 3 2009 Jun-Aug Jun-Aug Figure 3 Weinberg, La Parguera, Puerto Rico Figure 3 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.51334 28.6000 0.3873415 6 8 28.51334 28.600 0.3873415 sum 6 summer 2.5102190 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3878632 301.90 0.000000
421 EI0430 CD068 SI122 0.0000000 % 0 Figure 6 2008 Dec-Feb Dec-Feb Figure 6 Pelotas, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4799805 301.94 1.035709
422 EI0431 CD068 SI122 0.0000000 % 0 Figure 6 2008 Jun-Aug Jun-Aug Figure 6 Pelotas, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.6699829 301.94 0.000000
423 EI0432 CD068 SI122 0.0000000 % 0 Figure 6 2009 Dec-Feb Dec-Feb Figure 6 Pelotas, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2275162 301.94 0.000000
424 EI0433 CD068 SI122 0.0000000 % 0 Figure 6 2009 Jun-Aug Jun-Aug Figure 6 Pelotas, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3103561 301.94 1.035709
425 EI0434 CD068 SI123 0.0000000 % 0 Figure 6 2008 Dec-Feb Dec-Feb Figure 6 Enrique, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4671326 301.93 0.000000
426 EI0435 CD068 SI123 0.0000000 % 0 Figure 6 2008 Jun-Aug Jun-Aug Figure 6 Enrique, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.7207184 301.93 0.000000
427 EI0436 CD068 SI123 0.0000000 % 0 Figure 6 2009 Dec-Feb Dec-Feb Figure 6 Enrique, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2192841 301.93 0.000000
428 EI0437 CD068 SI123 0.0000000 % 0 Figure 6 2009 Jun-Aug Jun-Aug Figure 6 Enrique, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Inner-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
429 EI0438 CD068 SI124 15.9183673 % 5.9183673 Figure 6 2008 Dec-Feb Dec-Feb Figure 6 Media Luna, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 20.5018259 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4671326 301.93 0.000000
430 EI0439 CD068 SI124 12.7040816 % 4.2346939 Figure 6 2008 Jun-Aug Jun-Aug Figure 6 Media Luna, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 14.6694099 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.7207184 301.93 0.000000
431 EI0440 CD068 SI124 2.2959184 % 1.0204082 Figure 6 2009 Dec-Feb Dec-Feb Figure 6 Media Luna, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 3.5347976 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2192841 301.93 0.000000
432 EI0441 CD068 SI124 0.9693878 % 0.5102041 Figure 6 2009 Jun-Aug Jun-Aug Figure 6 Media Luna, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 Materials and Methods Mid-Shelf Reefs p83 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 1.7673988 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
433 EI0442 CD068 SI125 22.0408163 % 3.622449 Figure 6 2008 Dec-Feb Dec-Feb Figure 6 Turrumote, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.44834 26.2800 0.5424561 12 2 28.48933 28.395 0.4313078 aut 12 winter 12.5485314 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4671326 301.93 0.000000
434 EI0443 CD068 SI125 15.6122449 % 4.0306122 Figure 6 2008 Jun-Aug Jun-Aug Figure 6 Turrumote, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.48933 28.3950 0.4313078 6 8 28.48933 28.395 0.4313078 sum 6 summer 13.9624504 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.7207184 301.93 0.000000
435 EI0444 CD068 SI125 24.7448980 % 4.1326531 Figure 6 2009 Dec-Feb Dec-Feb Figure 6 Turrumote, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.28600 27.1280 0.5910563 12 2 28.49167 28.575 0.3867924 aut 12 winter 14.3159301 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.2192841 301.93 0.000000
436 EI0445 CD068 SI125 24.4387755 % 4.1326531 Figure 6 2009 Jun-Aug Jun-Aug Figure 6 Turrumote, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 Materials and Methods Mid-Shelf Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.49167 28.5750 0.3867924 6 8 28.49167 28.575 0.3867924 sum 6 summer 14.3159301 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3128586 301.93 0.000000
437 EI0446 CD068 SI126 0.0000000 % 0 Figure 6 2008 Dec-Feb Dec-Feb Figure 6 Buoy, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.47533 26.2830 0.5588973 12 2 28.52867 28.443 0.4624894 aut 12 winter 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4467773 301.90 0.000000
438 EI0447 CD068 SI126 1.0714286 % 0.1020408 Figure 6 2008 Jun-Aug Jun-Aug Figure 6 Buoy, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.52867 28.4430 0.4624894 6 8 28.52867 28.443 0.4624894 sum 6 summer 0.3534798 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.6807251 301.90 0.000000
439 EI0448 CD068 SI126 0.5612245 % 0.1530612 Figure 6 2009 Dec-Feb Dec-Feb Figure 6 Buoy, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.32600 27.1650 0.5902057 12 2 28.51334 28.600 0.3873415 aut 12 winter 0.5302196 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.1971588 301.90 0.000000
440 EI0449 CD068 SI126 1.7857143 % 0.255102 Figure 6 2009 Jun-Aug Jun-Aug Figure 6 Buoy, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.885167 -66.99183 17.885167 -66.99183 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.51334 28.6000 0.3873415 6 8 28.51334 28.600 0.3873415 sum 6 summer 0.8836994 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3878632 301.90 0.000000
441 EI0450 CD068 SI127 2.0408163 % 0.1020408 Figure 6 2008 Dec-Feb Dec-Feb Figure 6 Weinberg, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 26.47533 26.2830 0.5588973 12 2 28.52867 28.443 0.4624894 aut 12 winter 0.3534798 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.4467773 301.90 0.000000
442 EI0451 CD068 SI127 0.0000000 % 0 Figure 6 2008 Jun-Aug Jun-Aug Figure 6 Weinberg, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.52867 28.4430 0.4624894 6 8 28.52867 28.443 0.4624894 sum 6 summer 0.0000000 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.6807251 301.90 0.000000
443 EI0452 CD068 SI127 0.4591837 % 0.5102041 Figure 6 2009 Dec-Feb Dec-Feb Figure 6 Weinberg, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 27.32600 27.1650 0.5902057 12 2 28.51334 28.600 0.3873415 aut 12 winter 1.7673988 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.1971588 301.90 0.000000
444 EI0453 CD068 SI127 0.2040816 % 0.3061224 Figure 6 2009 Jun-Aug Jun-Aug Figure 6 Weinberg, La Parguera, Puerto Rico Figure 6 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 Materials and Methods Shelf-Edge Reefs p84 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 12 Atlantic Ocean 28.51334 28.6000 0.3873415 6 8 28.51334 28.600 0.3873415 sum 6 summer 1.0604393 NA NA 0.24000 1 Figure 1 NA 0 NA Belt 12 10.0000 2.00 20.0000 Materials and Methods Temporal and spatial variability in prevalence of CYBD in O. faveolata and O. franski p85 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 in paper as “caribbean yellow band disease” classified as YBD 0.3878632 301.90 0.000000
445 EI0454 CD069 SI128 74.6800000 % 3.61 Results p23 2013 May May Materials and Methods Study area p21 Panjang Island, Java Sea, Indonesia Figure 1 Coral Triangle & SE Asia Southeast Asia -6.579083 110.62910 -6.579083 110.62910 Materials and Methods Study area p21, GoogleMaps NA NA NA Pacific Ocean 30.12000 30.1200 NA 5 5 28.86267 28.708 0.4791047 aut 11 fall NA NA NA 0.10000 1 Figure 1 287 1 coral_n aggregated over whole study Belt 2 25.0000 2.00 50.0000 Materials and Methods Survey method p22 NA 4 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 White plague classified as WS 0.4914398 303.01 0.000000
446 EI0455 CD069 SI128 74.0700000 % 8.39 Results p23 2013 Nov Nov Materials and Methods Study area p21 Panjang Island, Java Sea, Indonesia Figure 1 Coral Triangle & SE Asia Southeast Asia -6.579083 110.62910 -6.579083 110.62910 Materials and Methods Study area p21, GoogleMaps NA NA NA Pacific Ocean 29.86300 29.8630 NA 11 11 28.86267 28.708 0.4791047 spr 5 spring NA NA NA 0.10000 1 Figure 1 287 1 coral_n aggregated over whole study Belt 2 25.0000 2.00 50.0000 Materials and Methods Survey method p22 NA 4 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 White plague classified as WS 0.4360886 303.01 0.000000
447 EI0456 CD070 SI129 2.5781250 % NA Figure 4 2010 Jan Jan Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 27.78000 27.7800 NA 1 1 28.84533 28.833 0.4036409 win 1 winter NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.8999939 303.61 1.017137
448 EI0457 CD070 SI129 3.6718750 % NA Figure 4 2010 Feb Feb Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 28.17800 28.1780 NA 2 2 28.84533 28.833 0.4036409 win 2 winter NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.8999939 303.61 1.017137
449 EI0458 CD070 SI129 4.6093750 % NA Figure 4 2010 Mar Mar Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 29.39500 29.3950 NA 3 3 28.84533 28.833 0.4036409 spr 3 spring NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.8999939 303.61 1.017137
450 EI0459 CD070 SI129 6.1718750 % NA Figure 4 2010 Apr Apr Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 30.44500 30.4450 NA 4 4 28.84533 28.833 0.4036409 spr 4 spring NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 2.2217560 303.61 1.017137
451 EI0460 CD070 SI129 8.2031250 % NA Figure 4 2010 May May Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 30.40800 30.4080 NA 5 5 28.84533 28.833 0.4036409 spr 5 spring NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.3814392 303.61 4.149999
452 EI0461 CD070 SI129 9.7656250 % NA Figure 4 2010 Jun Jun Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 29.25500 29.2550 NA 6 6 28.84533 28.833 0.4036409 sum 6 summer NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.3814392 303.61 4.149999
453 EI0462 CD070 SI129 11.2500000 % NA Figure 4 2010 Jul Jul Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 28.83300 28.8330 NA 7 7 28.84533 28.833 0.4036409 sum 7 summer NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.0507050 303.61 4.149999
454 EI0463 CD070 SI129 12.2656250 % NA Figure 4 2010 Aug Aug Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 28.44800 28.4480 NA 8 8 28.84533 28.833 0.4036409 sum 8 summer NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.0507050 303.61 4.149999
455 EI0464 CD070 SI129 13.1250000 % NA Figure 4 2010 Sep Sep Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 28.52000 28.5200 NA 9 9 28.84533 28.833 0.4036409 aut 9 fall NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.1628723 303.61 4.149999
456 EI0465 CD070 SI129 13.9062500 % NA Figure 4 2010 Oct Oct Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 28.56500 28.5650 NA 10 10 28.84533 28.833 0.4036409 aut 10 fall NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.1628723 303.61 4.149999
457 EI0466 CD070 SI129 14.4531250 % NA Figure 4 2010 Nov Nov Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 28.29300 28.2930 NA 11 11 28.84533 28.833 0.4036409 aut 11 fall NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.1628723 303.61 4.149999
458 EI0467 CD070 SI129 14.9218750 % NA Figure 4 2010 Dec Dec Figure 4 Palk Bay, Indian Ocean Figure 1 Eastern Indian Ocean Central Indian 9.283333 79.21389 9.283333 79.21389 Materials and Methods Study site p64, averaged two longitudes, GoogleMaps conversion to decimal NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 40 Indian Ocean 27.59500 27.5950 NA 12 12 28.84533 28.833 0.4036409 win 12 winter NA NA NA 3.20000 5 Figure 1 1930 1 NA Belt 40 20.0000 4.00 80.0000 Materials and Methods Field data collection p65 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acropora white syndrome categorized as WS 0.3614502 303.61 4.149999
459 EI0468 CD034 SI130 19.1000000 % 0.04 Results Coral health p299 2007 Oct-Dec Oct-Dec Methods In situ sampling protocols p293 Hind Bank Marine Conservation District, Puerto Rican Shelf, US Virgin Islands Methods Study location and sampling stratification p291 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.219110 -65.03766 18.219110 -65.03766 GoogleMaps Hind Bank Marine Conservation District NA NA NA Atlantic Ocean 28.08700 28.1500 0.8941660 10 12 28.82534 28.913 0.2153284 aut 10 fall NA NA NA 2.40000 80 Methods Study location and sampling stratification p291 1251 1 NA Line 80 30.0000 1.00 30.0000 Methods In situ sampling protocols p292 Assume 1 transect per site because not stated otherwise 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 White plague classified as WS 0.2989120 301.60 2.192823
460 EI0469 CD071 SI131 1.3975155 % 0.26708075 Figure 3b 2011 Feb Feb Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 25.47619 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 24.47800 24.4780 NA 2 2 25.54367 25.470 0.2993762 win 2 winter 1.6891669 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.2864227 300.05 0.000000
461 EI0470 CD071 SI131 1.3105590 % 0.28571429 Figure 3b 2011 Mar Mar Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 24.80952 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 24.42300 24.4230 NA 3 3 25.54367 25.470 0.2993762 spr 3 spring 1.8070158 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.2864227 300.05 0.000000
462 EI0471 CD071 SI131 1.6894410 % 0.28571429 Figure 3b 2011 Apr Apr Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 26.58333 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 24.82300 24.8230 NA 4 4 25.54367 25.470 0.2993762 spr 4 spring 1.8070158 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.1878510 300.05 0.000000
463 EI0472 CD071 SI131 2.0621118 % 0.36024845 Figure 3b 2011 May May Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 25.85714 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 25.24800 25.2480 NA 5 5 25.54367 25.470 0.2993762 spr 5 spring 2.2784112 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.1878510 300.05 0.000000
464 EI0473 CD071 SI131 2.4534161 % 0.31677019 Figure 3b 2011 Jun Jun Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 26.00000 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 25.28800 25.2880 NA 6 6 25.54367 25.470 0.2993762 sum 6 summer 2.0034306 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.1764221 300.05 0.000000
465 EI0474 CD071 SI131 1.6397516 % 0.26086957 Figure 3b 2011 Aug Aug Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 26.57143 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 25.87300 25.8730 NA 8 8 25.54367 25.470 0.2993762 sum 8 summer 1.6498840 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.1764221 300.05 0.000000
466 EI0475 CD071 SI131 2.0310559 % 0.35403727 Figure 3b 2011 Sep Sep Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 26.88095 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 26.41500 26.4150 NA 9 9 25.54367 25.470 0.2993762 aut 9 fall 2.2391283 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.4264450 300.05 0.000000
467 EI0476 CD071 SI131 1.2422360 % 0.37267081 Figure 3b 2011 Oct Oct Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 25.75000 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 26.34800 26.3480 NA 10 10 25.54367 25.470 0.2993762 aut 10 fall 2.3569771 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.1285706 300.05 0.000000
468 EI0477 CD071 SI131 0.6273292 % 0.22360248 Figure 3b 2011 Nov Nov Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 23.92857 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 25.56000 25.5600 NA 11 11 25.54367 25.470 0.2993762 aut 11 fall 1.4141863 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.2021484 300.05 0.000000
469 EI0478 CD071 SI131 0.5155280 % 0.1863354 Figure 3b 2011 Dec Dec Figure 3b Coconut Island Marine Reserve, Kaneohe Bay, Hawaii Materials and Methods Prevalence and spatial distribution p60 Western Pacific Polynesia 21.433333 -157.78333 21.433333 -157.78333 Materials and Methods Prevalence and spatial distribution p60, GoogleMaps 23.40476 Fig 3b Prevalence and temperature extracted using MetaDigitise in Rstudio, n = 40 Pacific Ocean 24.49800 24.4980 NA 12 12 25.54367 25.470 0.2993762 win 12 winter 1.1784886 NA NA 0.80000 8 Figure 2 NA 0 NA Belt 40 10.0000 2.00 20.0000 Materials and Method Prevalence and spatial distribution p60-61 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NA 0.0843048 300.05 0.000000
470 EI0479 CD072 SI132 28.0000000 % 11 Results Growth anomalies (GAs) on corals 2012 Jul Jul Materials and Methods Qeshm Island Materials and Methods Western Indian Ocean Middle East 26.688974 55.94352 26.688974 55.94352 Figure 1, GoogleMaps NA NA NA Indian Ocean 32.56000 32.5600 NA 7 7 31.97200 32.560 1.0384282 sum 7 summer NA NA NA 0.24000 1 Figure 1 NA 1 NA Belt 8 30.0000 1.00 30.0000 Materials and Methods NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3781967 305.68 0.000000
471 EI0480 CD072 SI132 21.0000000 % 13 Results Growth anomalies (GAs) on corals 2012 Jul Jul Materials and Methods Qeshm Island Materials and Methods Western Indian Ocean Middle East 26.688974 55.94352 26.688974 55.94352 Figure 1, GoogleMaps NA NA NA Indian Ocean 32.56000 32.5600 NA 7 7 31.97200 32.560 1.0384282 sum 7 summer NA NA NA 0.24000 1 Figure 1 NA 1 NA Belt 8 30.0000 1.00 30.0000 Materials and Methods NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3781967 305.68 0.000000
472 EI0481 CD072 SI132 37.0000000 % 7 Results Growth anomalies (GAs) on corals 2013 Jul-Aug Jul-Aug Materials and Methods Qeshm Island Materials and Methods Western Indian Ocean Middle East 26.688974 55.94352 26.688974 55.94352 Figure 1, GoogleMaps NA NA NA Indian Ocean 31.77150 31.7715 0.2524360 7 8 31.36767 31.593 0.7218769 sum 7 summer NA NA NA 0.24000 1 Figure 1 NA 1 NA Belt 8 30.0000 1.00 30.0000 Materials and Methods NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7435761 305.68 0.000000
473 EI0482 CD072 SI132 23.0000000 % 12 Results Growth anomalies (GAs) on corals 2013 Jul-Aug Jul-Aug Materials and Methods Qeshm Island Materials and Methods Western Indian Ocean Middle East 26.688974 55.94352 26.688974 55.94352 Figure 1, GoogleMaps NA NA NA Indian Ocean 31.77150 31.7715 0.2524360 7 8 31.36767 31.593 0.7218769 sum 7 summer NA NA NA 0.24000 1 Figure 1 NA 1 NA Belt 8 30.0000 1.00 30.0000 Materials and Methods NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7435761 305.68 0.000000
474 EI0483 CD073 SI133 0.7700000 % 0.23 Table 1 2016 Jan-Apr Jan-Apr Materials and Methods p212 Johnston Atoll Materials and Methods p212 Western Pacific Micronesia 16.757160 -169.53308 16.757160 -169.53308 GoogleMaps Johnston Atoll NA NA NA Pacific Ocean 26.09325 25.9625 0.2278867 1 4 28.00167 27.930 0.5808258 spr 1 winter NA NA NA 0.05000 18 Materials and Methods p212 NA 1 NA Belt 2 25.0000 1.00 25.0000 Materials and Methods p214 NA 3 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7414246 301.15 6.192819
475 EI0484 CD073 SI134 0.1600000 % 0.05 Table 1 2017 Apr-May Apr-May Materials and Methods p212 Wake Atoll Materials and Methods p212 Western Pacific Micronesia 22.572930 166.45558 22.572930 166.45558 GoogleMaps Wake Atoll NA NA NA Pacific Ocean 26.38150 26.3815 0.3938593 4 5 28.90533 29.133 0.5221560 spr 4 spring NA NA NA 0.15000 12 Materials and Methods p212 NA 1 NA Belt 2 25.0000 3.00 75.0000 Materials and Methods p214 NA 3 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0414352 301.67 2.225706
476 EI0485 CD073 SI135 0.0600000 % 0.01 Table 1 2016 Jan-Apr Jan-Apr Materials and Methods p212 Baker Island Materials and Methods p212 Western Pacific Micronesia 4.760190 -177.53317 4.760190 -177.53317 GoogleMaps Baker Island NA NA NA Pacific Ocean 28.91000 28.8550 0.1253655 1 4 29.63433 29.615 0.1023782 spr 1 winter NA NA NA 0.15000 6 Materials and Methods p212 NA 1 NA Belt 2 25.0000 3.00 75.0000 Materials and Methods p214 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1943359 302.52 0.000000
477 EI0486 CD073 SI136 0.0300000 % 0.02 Table 1 2016 Jan-Apr Jan-Apr Materials and Methods p212 Howland Island Materials and Methods p212 Western Pacific Micronesia 4.844480 -178.02393 4.844480 -178.02393 GoogleMaps Howland Island NA NA NA Pacific Ocean 28.87850 28.8230 0.1387244 1 4 29.64933 29.625 0.1134736 spr 1 winter NA NA NA 0.15000 8 Materials and Methods p212 NA 1 NA Belt 2 25.0000 3.00 75.0000 Materials and Methods p214 NA 2 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8343048 302.51 0.000000
478 EI0487 CD073 SI137 0.0400000 % 0.02 Table 1 2016 Jan-Apr Jan-Apr Materials and Methods p212 Jarvis Island Materials and Methods p212 Western Pacific Micronesia 5.070120 -159.99918 5.070120 -159.99918 GoogleMaps Jarvis Island NA NA NA Pacific Ocean 28.69200 28.6440 0.0791370 1 4 28.92367 28.930 0.1575958 spr 1 winter NA NA NA 0.15000 9 Materials and Methods p212 NA 1 NA Belt 2 25.0000 3.00 75.0000 Materials and Methods p214 NA 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 “other” category marked as unknown 0.6857300 301.84 23.389924
479 EI0488 CD073 SI138 0.0400000 % 0.01 Table 1 2016 Jan-Apr Jan-Apr Materials and Methods p212 Palmyra Atoll Materials and Methods p212 Western Pacific Micronesia 5.890000 -162.07848 5.890000 -162.07848 GoogleMaps Palmyra Atoll NA NA NA Pacific Ocean 28.69475 28.5955 0.1223614 1 4 29.05033 29.083 0.1271859 spr 1 winter NA NA NA 0.15000 13 Materials and Methods p212 NA 1 NA Belt 2 25.0000 3.00 75.0000 Materials and Methods p214 NA 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 “other” category marked as unknown 0.7907867 302.10 5.595706
480 EI0489 CD073 SI139 0.0400000 % 0.01 Table 1 2016 Jan-Apr Jan-Apr Materials and Methods p212 Kingman Reef Materials and Methods p212 Western Pacific Micronesia 6.487120 -162.41682 6.487120 -162.41682 GoogleMaps Kingman Reef NA NA NA Pacific Ocean 28.48225 28.3830 0.1399251 1 4 29.01033 28.990 0.0191398 spr 1 winter NA NA NA 0.15000 14 Materials and Methods p212 NA 1 NA Belt 2 25.0000 3.00 75.0000 Materials and Methods p214 NA 3 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4168015 302.13 1.187139
481 EI0490 CD074 SI140 17.0909100 % 2 Figure 2 2012 Jun Jun Figure 2 Pickles Reef, Upper Florida Keys Materials and Methods Study site and experimental nutrient treatments p545 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.001389 -80.41528 25.001389 -80.41528 Materials and Methods Study site and experimental nutrient treatments p545, GoogleMaps 28.13000 Discussion Nutrient-induced bleaching p552 Prevalence extracted using MetaDigitise in Rstudio, n = 5 Atlantic Ocean 27.81800 27.8180 NA 6 6 28.84867 29.085 0.9351722 sum 6 summer 4.4721360 0.44 NA 0.39500 1 Materials and Methods Study site and experimental nutrient treatments p545 455 0 NA Circle 5 5.0000 NA 79.0000 Materials and Methods Disease surveys p546 NA 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DSS main disease found, also noted BBD; most likely others present as well, but only these mentioned; effect size is overall prevalence of disease 0.7510910 302.76 7.185701
482 EI0491 CD074 SI140 21.0909100 % 7.272727 Figure 2 2012 Jun Jun Figure 2 Pickles Reef, Upper Florida Keys Materials and Methods Study site and experimental nutrient treatments p545 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.001389 -80.41528 25.001389 -80.41528 Materials and Methods Study site and experimental nutrient treatments p545, GoogleMaps 28.13000 Discussion Nutrient-induced bleaching p552 Prevalence extracted using MetaDigitise in Rstudio, n = 4 Atlantic Ocean 27.81800 27.8180 NA 6 6 28.84867 29.085 0.9351722 sum 6 summer 14.5454550 0.44 NA 0.31600 1 Materials and Methods Study site and experimental nutrient treatments p545 370 0 NA Circle 4 5.0000 NA 79.0000 Materials and Methods Disease surveys p546 NA 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DSS main disease found, also noted BBD; most likely others present as well, but only these mentioned; effect size is overall prevalence of disease 0.7510910 302.76 7.185701
483 EI0492 CD074 SI140 14.0000000 % 3.090909 Figure 2 2013 Feb Feb Figure 2 Pickles Reef, Upper Florida Keys Materials and Methods Study site and experimental nutrient treatments p545 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.001389 -80.41528 25.001389 -80.41528 Materials and Methods Study site and experimental nutrient treatments p545, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 4 Atlantic Ocean 24.03000 24.0300 NA 2 2 28.38267 28.420 0.8066489 win 2 winter 6.1818180 NA NA 0.31600 1 Materials and Methods Study site and experimental nutrient treatments p545 370 0 NA Circle 4 5.0000 NA 79.0000 Materials and Methods Disease surveys p546 NA 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DSS main disease found, also noted BBD; most likely others present as well, but only these mentioned; effect size is overall prevalence of disease 0.5842896 302.76 0.000000
484 EI0493 CD074 SI140 28.3636400 % 4 Figure 2 2013 Jun Jun Figure 2 Pickles Reef, Upper Florida Keys Materials and Methods Study site and experimental nutrient treatments p545 Caribbean & Gulf of Mexico Caribbean/Atlantic 25.001389 -80.41528 25.001389 -80.41528 Materials and Methods Study site and experimental nutrient treatments p545, GoogleMaps 27.79000 Discussion Nutrient-induced bleaching p552 Prevalence extracted using MetaDigitise in Rstudio, n = 4 Atlantic Ocean 27.55800 27.5580 NA 6 6 28.38267 28.420 0.8066489 sum 6 summer 8.0000000 0.16 NA 0.31600 1 Materials and Methods Study site and experimental nutrient treatments p545 370 0 NA Circle 4 5.0000 NA 79.0000 Materials and Methods Disease surveys p546 NA 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DSS main disease found, also noted BBD; most likely others present as well, but only these mentioned; effect size is overall prevalence of disease 0.3082123 302.76 0.000000
485 EI0494 CD075 SI141 0.2600000 % 0.11 Table 2 2012 May-Sep May-Sep Materials and Methods Coral monitoring surveys Southeast Florida Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 26.225525 -80.07882 26.225525 -80.07882 Table 1 median site, GoogleMaps NA NA NA Atlantic Ocean 28.42940 29.0630 1.2843025 5 9 28.78800 29.063 0.9672772 multi 5 spring NA NA NA 1.40800 16 Materials and Methods Coral monitoring surveys NA 1 NA Belt 64 22.0000 1.00 22.0000 Materials and Mtehods Coral monitoring surveys NA 5 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Classified in paper as “WS” vs “Non-white syndrome” (groups BBD, YBD, WB (acroporids only), and DSS) 0.3857117 302.58 1.062853
486 EI0495 CD075 SI141 0.5100000 % 0.23 Table 2 2013 May-Sep May-Sep Materials and Methods Coral monitoring surveys Southeast Florida Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 26.225525 -80.07882 26.225525 -80.07882 Table 1 median site, GoogleMaps NA NA NA Atlantic Ocean 28.06760 28.2730 1.3560471 5 9 28.32433 28.273 0.8062260 multi 5 spring NA NA NA 1.93600 22 Materials and Methods Coral monitoring surveys NA 1 NA Belt 88 22.0000 1.00 22.0000 Materials and Mtehods Coral monitoring surveys NA 5 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Classified in paper as “WS” vs “Non-white syndrome” (groups BBD, YBD, WB (acroporids only), and DSS) 0.7617950 302.58 0.000000
487 EI0496 CD075 SI141 1.2400000 % 0.34 Table 2 2014 May-Sep May-Sep Materials and Methods Coral monitoring surveys Southeast Florida Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 26.225525 -80.07882 26.225525 -80.07882 Table 1 median site, GoogleMaps NA NA NA Atlantic Ocean 28.68480 29.4980 1.4043619 5 9 29.15867 29.498 1.0701410 multi 5 spring NA NA NA 1.93600 22 Materials and Methods Coral monitoring surveys NA 1 NA Belt 88 22.0000 1.00 22.0000 Materials and Mtehods Coral monitoring surveys NA 5 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Classified in paper as “WS” vs “Non-white syndrome” (groups BBD, YBD, WB (acroporids only), and DSS) 0.2692871 302.58 0.000000
488 EI0497 CD075 SI141 1.4900000 % 0.35 Table 2 2015 May-Sep May-Sep Materials and Methods Coral monitoring surveys Southeast Florida Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 26.225525 -80.07882 26.225525 -80.07882 Table 1 median site, GoogleMaps NA NA NA Atlantic Ocean 29.10980 29.4880 0.8509532 5 9 29.43200 29.488 0.4436586 multi 5 spring NA NA NA 1.93600 22 Materials and Methods Coral monitoring surveys NA 1 NA Belt 88 22.0000 1.00 22.0000 Materials and Mtehods Coral monitoring surveys NA 5 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Classified in paper as “WS” vs “Non-white syndrome” (groups BBD, YBD, WB (acroporids only), and DSS) 0.8671570 302.58 2.194280
489 EI0498 CD075 SI141 3.2900000 % 0.6 Table 2 2016 May-Sep May-Sep Materials and Methods Coral monitoring surveys Southeast Florida Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 26.225525 -80.07882 26.225525 -80.07882 Table 1 median site, GoogleMaps NA NA NA Atlantic Ocean 29.04580 30.0950 1.3456276 5 9 29.55100 30.095 0.8223307 multi 5 spring NA NA NA 1.93600 22 Materials and Methods Coral monitoring surveys NA 1 NA Belt 88 22.0000 1.00 22.0000 Materials and Mtehods Coral monitoring surveys NA 5 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Classified in paper as “WS” vs “Non-white syndrome” (groups BBD, YBD, WB (acroporids only), and DSS) 0.4435806 302.58 0.000000
490 EI0499 CD076 SI142 5.7000000 % 0.8 Results p26 2002 0 0 Results p26 Akumal, Mexico Materials and Methods p24 Caribbean & Gulf of Mexico Caribbean/Atlantic 20.607490 -87.34901 20.394070 -87.31370 GoogleMaps Akumal Mexico NA NA NA Atlantic Ocean 29.07300 29.0730 NA 7 7 29.11933 29.073 0.3895717 sum 7 summer NA NA NA 1.50000 10 Materials and Methods p24 NA 1 NA Belt 30 25.0000 2.00 50.0000 Materials and Methods p24 NA 6 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1728516 301.86 0.000000
491 EI0500 CD076 SI142 7.9600000 % 0.7 Results p26 2004 0 0 Results p26 Akumal, Mexico Materials and Methods p24 Caribbean & Gulf of Mexico Caribbean/Atlantic 20.607490 -87.34901 20.394070 -87.31370 GoogleMaps Akumal Mexico NA NA NA Atlantic Ocean 28.88800 28.8880 NA 7 7 28.89033 28.888 0.3115062 sum 7 summer NA NA NA 1.50000 10 Materials and Methods p24 NA 1 NA Belt 30 25.0000 2.00 50.0000 Materials and Methods p24 NA 6 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3696365 301.86 0.000000
492 EI0501 CD077 SI143 9.7000000 % NA Table 2 2010 Sep Sep Materials and Methods p164 Zamami, Ryukyu Archipelago, Japan Table 2 Western Pacific Southeast Asia 26.201500 127.32083 26.201500 127.32083 Materials and Method p164 first coordinates, GoogleMaps NA NA NA Pacific Ocean 29.05300 29.0530 NA 9 9 28.34200 29.003 1.5789252 aut 9 fall 7.9000000 NA NA 0.60000 2 Materials and Methods p164 NA 1 NA Belt 6 50.0000 2.00 100.0000 Materials and Methods p164 NA 5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 “Compromised health” categorized as unknown 0.5392838 301.63 1.009985
493 EI0502 CD077 SI144 3.6000000 % NA Table 2 2010 Sep Sep Materials and Methods p164 Ooyama, Ryukyu Archipelago, Japan Table 2 Western Pacific Southeast Asia 26.298500 127.72167 26.298500 127.72167 Materials and Method p164 first coordinates, GoogleMaps NA NA NA Pacific Ocean 29.05300 29.0530 NA 9 9 28.34200 29.003 1.5789252 aut 9 fall 4.6000000 NA NA 0.60000 2 Materials and Methods p164 NA 1 NA Belt 6 50.0000 2.00 100.0000 Materials and Methods p164 NA 5 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9203339 301.72 1.065706
494 EI0503 CD078 SI145 2.1578950 % 0.5789474 Figure 2 2016 Nov-Jan Nov-Jan Methods Sampling location and data collection Southern Eleuthera, The Bahamas Methods Sampling location and data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.749246 -76.26184 24.749246 -76.26184 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 33 Atlantic Ocean 26.41600 26.4900 0.8583956 11 1 29.33334 29.635 0.5841733 aut 11 fall 3.3257990 NA NA 0.33000 5 Methods Sampling location and data collection 1232 1 NA Belt 33 10.0000 1.00 10.0000 Methods Sampling location and data collection NA 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8839569 302.30 3.377132
495 EI0504 CD079 SI146 20.5000000 % 0.7 Results 3.1. Spatial patterns of P. lobata growth anomalies and coral cover 2013 Jun-Jul Jun-Jul Methods 2.3. Enterococci assays Puako region, Hawaii Methods 2.1. The Puako region and study sites Western Pacific Polynesia 19.951950 -155.87206 19.951950 -155.87206 GoogleMaps Puako, HI NA NA NA Pacific Ocean 25.57550 25.5755 0.3429459 6 7 25.77700 25.818 0.4249850 sum 6 summer NA NA NA 0.45000 10 Methods 2.2. Coral cover and disease surveys NA 0 NA Belt 30 15.0000 1.00 15.0000 Methods 2.2. Coral cover and disease surveys NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “Porites lobata growth anomalies” categorized as GA 0.1414337 300.37 0.000000
496 EI0505 CD081 SI147 4.3478260 % 2.839988 Figure 4 2008 Oct Oct Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps 29.54000 Table 3 Prevalence extracted using MetaDigitise in Rstudio, n = 6; coordinates not found in SST database, reselected nearby coordinates using GoogleMaps Atlantic Ocean 29.35800 29.3580 NA 10 10 27.79434 27.610 0.6097677 aut 10 fall 6.9565220 NA 0.3400 0.06000 1 Figure 1 35 1 NA Belt 6 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2342834 301.32 7.221392
497 EI0506 CD081 SI147 5.0000000 % 4.891304 Figure 4 2010 Feb Feb Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps 27.00000 Table 3 Prevalence extracted using MetaDigitise in Rstudio, n = 4; coordinates not found in SST database, reselected nearby coordinates using GoogleMaps Atlantic Ocean 27.04000 27.0400 NA 2 2 29.05633 28.853 0.3265474 win 2 winter 9.7826090 NA 0.5600 0.04000 1 Figure 1 18 1 NA Belt 4 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1699982 301.32 0.000000
498 EI0507 CD081 SI147 16.0869570 % 5.89367 Figure 4 2010 Sep Sep Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps 29.51000 Table 3 Prevalence extracted using MetaDigitise in Rstudio, n = 15; coordinates not found in SST database, reselected nearby coodinatesusing GoogleMaps Atlantic Ocean 29.81500 29.8150 NA 9 9 29.05633 28.853 0.3265474 aut 9 fall 22.8260870 NA 0.3500 0.15000 1 Figure 1 107 1 NA Belt 15 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3571167 301.32 4.492830
499 EI0508 CD081 SI147 14.5652170 % 4.125119 Figure 4 2012 Sep Sep Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps 29.02000 Table 3 Prevalence extracted using MetaDigitise in Rstudio, n = 14; coordinates not found in SST database, reselected nearby coordinates using GoogleMaps Atlantic Ocean 28.99500 28.9950 NA 9 9 28.00767 27.875 0.1977904 aut 9 fall 15.4347830 NA 0.4300 0.14000 1 Figure 1 185 1 NA Belt 14 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.0907135 301.32 0.000000
500 EI0509 CD081 SI147 1.7391300 % 1.069424 Figure 4 2014 Sep Sep Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 5; coordinates not found in SST database, reselected nearby coordinate using GoogleMaps Atlantic Ocean 28.46300 28.4630 NA 9 9 27.55000 27.445 0.3834378 aut 9 fall 2.3913040 NA NA 0.05000 1 Figure 1 143 1 NA Belt 5 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2232208 301.32 1.105721
501 EI0510 CD081 SI147 5.6521740 % 4.673913 Figure 4 2015 Mar Mar Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 4; coordinates not found in SST database, reselected nearby coordinatesusing GoogleMaps Atlantic Ocean 26.66500 26.6650 NA 3 3 27.42200 27.270 0.3552859 spr 3 spring 9.3478260 NA NA 0.04000 1 Figure 1 137 1 NA Belt 4 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.0442963 301.32 0.000000
502 EI0511 CD081 SI147 4.3478260 % 2.30749 Figure 4 2016 Mar Mar Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 6; coordinates not found in SST database, reselected nearby coordinatesusing GoogleMaps Atlantic Ocean 26.93500 26.9350 NA 3 3 28.22700 28.073 0.4635980 spr 3 spring 5.6521740 NA NA 0.06000 1 Figure 1 118 1 NA Belt 6 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5349884 301.32 1.092842
503 EI0512 CD081 SI147 0.0000000 % 0 Figure 4 2016 Sep Sep Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 4; coordinates not found in SST database, reselected nearby coordinatesusing GoogleMaps Atlantic Ocean 29.02300 29.0230 NA 9 9 28.22700 28.073 0.4635980 aut 9 fall 0.0000000 NA NA 0.04000 1 Figure 1 68 1 NA Belt 4 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8828735 301.32 1.092842
504 EI0513 CD081 SI147 1.5217390 % 1.195652 Figure 4 2017 Feb Feb Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 4; coordinates not found in SST database, reselected nearby coordinatesusing GoogleMaps Atlantic Ocean 26.92300 26.9230 NA 2 2 28.33867 28.058 0.5416056 win 2 winter 2.3913040 NA NA 0.04000 1 Figure 1 77 1 NA Belt 4 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4785995 301.32 6.079996
505 EI0514 CD081 SI147 6.3043480 % 1.278206 Figure 4 2017 Sep-Oct Sep-Oct Table 2 Playa Lechi, Kralendijk, Bonaire Materials and methods Study site p3 Caribbean & Gulf of Mexico Caribbean/Atlantic 12.160167 -68.28203 12.159270 -68.34449 Materials and methods Study site p3, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 14; coordinates not found in SST database, reselected nearby coordinates using GoogleMaps Atlantic Ocean 29.21150 29.2115 0.1081873 9 10 28.33867 28.058 0.5416056 aut 9 fall 4.7826090 NA NA 0.14000 1 Figure 1 527 1 NA Belt 14 10.0000 1.00 10.0000 Table 2, Materials and Methods Disease prevalence and coral mortality p3, Fall 2017 data collection p4 NA 7 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2028656 301.32 6.079996
506 EI0515 CD082 SI148 2.7027030 % 0.6756757 Figure 4 2003 0 0 Materials and Methods Methods p223 Enrique, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 Materials and Methods Study area p222 NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.06800 28.0680 NA 7 7 28.15633 28.068 0.3851728 sum 7 summer 2.7027030 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.4235764 301.93 0.000000
507 EI0516 CD082 SI148 1.0810810 % 0.2702703 Figure 4 2004 0 0 Materials and Methods Methods p223 Enrique, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.15500 28.1550 NA 7 7 28.20100 28.155 0.3810876 sum 7 summer 1.0810810 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.1696777 301.93 0.000000
508 EI0517 CD082 SI148 8.2432430 % 1.4864865 Figure 4 2005 0 0 Materials and Methods Methods p223 Enrique, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.90800 28.9080 NA 7 7 28.94867 28.908 0.3080198 sum 7 summer 5.9459460 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.6728439 301.93 2.175707
509 EI0518 CD082 SI148 16.0810810 % 1.4864865 Figure 4 2006 0 0 Materials and Methods Methods p223 Enrique, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.49300 28.4930 NA 7 7 28.57100 28.493 0.1465026 sum 7 summer 5.9459460 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 1.3771362 301.93 9.668563
510 EI0519 CD082 SI148 4.7297300 % 0.4054054 Figure 4 2007 0 0 Materials and Methods Methods p223 Enrique, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.944300 -67.03688 17.944300 -67.03688 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.94500 28.9450 NA 7 7 28.89000 28.945 0.1934555 sum 7 summer 1.6216220 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.3389282 301.93 2.125688
511 EI0520 CD082 SI149 2.5675680 % 0.6756757 Figure 4 2003 0 0 Materials and Methods Methods p223 Pelotas, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.06800 28.0680 NA 7 7 28.15633 28.068 0.3851728 sum 7 summer 2.7027030 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.4250031 301.94 0.000000
512 EI0521 CD082 SI149 3.5135140 % 0.6756757 Figure 4 2004 0 0 Materials and Methods Methods p223 Pelotas, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.15500 28.1550 NA 7 7 28.20100 28.155 0.3810876 sum 7 summer 2.7027030 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.1607208 301.94 1.107150
513 EI0522 CD082 SI149 5.8108110 % 0.9459459 Figure 4 2005 0 0 Materials and Methods Methods p223 Pelotas, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.90800 28.9080 NA 7 7 28.94867 28.908 0.3080198 sum 7 summer 3.7837840 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.6764069 301.94 2.308539
514 EI0523 CD082 SI149 10.6756760 % 1.7567568 Figure 4 2006 0 0 Materials and Methods Methods p223 Pelotas, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.49300 28.4930 NA 7 7 28.57100 28.493 0.1465026 sum 7 summer 7.0270270 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 1.4200363 301.94 9.975699
515 EI0524 CD082 SI149 8.1081080 % 1.0810811 Figure 4 2007 0 0 Materials and Methods Methods p223 Pelotas, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.957367 -67.06960 17.957367 -67.06960 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.94500 28.9450 NA 7 7 28.89000 28.945 0.1934555 sum 7 summer 4.3243240 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.3371201 301.94 2.244269
516 EI0525 CD082 SI150 4.4594590 % 0.8108108 Figure 4 2003 0 0 Materials and Methods Methods p223 Turrumote, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.06800 28.0680 NA 7 7 28.15633 28.068 0.3851728 sum 7 summer 3.2432430 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.4235764 301.93 0.000000
517 EI0526 CD082 SI150 7.1621620 % 0.9459459 Figure 4 2004 0 0 Materials and Methods Methods p223 Turrumote, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.15500 28.1550 NA 7 7 28.20100 28.155 0.3810876 sum 7 summer 3.7837840 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.1696777 301.93 0.000000
518 EI0527 CD082 SI150 10.8108110 % 2.027027 Figure 4 2005 0 0 Materials and Methods Methods p223 Turrumote, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.90800 28.9080 NA 7 7 28.94867 28.908 0.3080198 sum 7 summer 8.1081080 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.6728439 301.93 2.175707
519 EI0528 CD082 SI150 25.4054050 % 2.5675676 Figure 4 2006 0 0 Materials and Methods Methods p223 Turrumote, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.49300 28.4930 NA 7 7 28.57100 28.493 0.1465026 sum 7 summer 10.2702700 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 1.3771362 301.93 9.668563
520 EI0529 CD082 SI150 20.4054050 % 2.1621622 Figure 4 2007 0 0 Materials and Methods Methods p223 Turrumote, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934950 -67.01883 17.934950 -67.01883 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.94500 28.9450 NA 7 7 28.89000 28.945 0.1934555 sum 7 summer 8.6486490 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.3389282 301.93 2.125688
521 EI0530 CD082 SI151 3.3783780 % 0.5405405 Figure 4 2003 0 0 Materials and Methods Methods p223 Media Luna, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.06800 28.0680 NA 7 7 28.15633 28.068 0.3851728 sum 7 summer 2.1621620 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.4235764 301.93 0.000000
522 EI0531 CD082 SI151 3.7837840 % 0.5405405 Figure 4 2004 0 0 Materials and Methods Methods p223 Media Luna, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.15500 28.1550 NA 7 7 28.20100 28.155 0.3810876 sum 7 summer 2.1621620 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.1696777 301.93 0.000000
523 EI0532 CD082 SI151 10.5405410 % 1.8918919 Figure 4 2005 0 0 Materials and Methods Methods p223 Media Luna, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.90800 28.9080 NA 7 7 28.94867 28.908 0.3080198 sum 7 summer 7.5675680 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.6728439 301.93 2.175707
524 EI0533 CD082 SI151 20.1351350 % 1.8918919 Figure 4 2006 0 0 Materials and Methods Methods p223 Media Luna, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.49300 28.4930 NA 7 7 28.57100 28.493 0.1465026 sum 7 summer 7.5675680 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 1.3771362 301.93 9.668563
525 EI0534 CD082 SI151 12.4324320 % 1.0810811 Figure 4 2007 0 0 Materials and Methods Methods p223 Media Luna, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.934883 -67.04885 17.934883 -67.04885 NA NA NA Prevalence extracted using MetaDigitise in Rstudio, n = 32 Atlantic Ocean 28.94500 28.9450 NA 7 7 28.89000 28.945 0.1934555 sum 7 summer 4.3243240 NA NA 2.56000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.3389282 301.93 2.125688
526 EI0535 CD082 SI152 9.2000000 % 0.6 Figure 4 2003 0 0 Materials and Methods Methods p223 Weinberg, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.05300 28.0530 NA 7 7 28.13967 28.053 0.4414280 sum 7 summer 2.4000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.3725128 301.90 0.000000
527 EI0536 CD082 SI152 2.2000000 % 0.2 Figure 4 2004 0 0 Materials and Methods Methods p223 Weinberg, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.18800 28.1880 NA 7 7 28.23267 28.188 0.4167989 sum 7 summer 0.8000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.2014465 301.90 1.024287
528 EI0537 CD082 SI152 4.4000000 % 0.6 Figure 4 2005 0 0 Materials and Methods Methods p223 Weinberg, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.95800 28.9580 NA 7 7 28.99467 28.958 0.3315251 sum 7 summer 2.4000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.6257095 301.90 1.148554
529 EI0538 CD082 SI152 11.0000000 % 2 Figure 4 2006 0 0 Materials and Methods Methods p223 Weinberg, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.52000 28.5200 NA 7 7 28.60600 28.520 0.1755787 sum 7 summer 8.0000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 1.2942810 301.90 8.489988
530 EI0539 CD082 SI152 8.8000000 % 2.6 Figure 4 2007 0 0 Materials and Methods Methods p223 Weinberg, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.890483 -66.98867 17.890483 -66.98867 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.96000 28.9600 NA 7 7 28.88667 28.960 0.2098410 sum 7 summer 10.4000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.2899933 301.90 1.108576
531 EI0540 CD082 SI153 10.4000000 % 0.8 Figure 4 2003 0 0 Materials and Methods Methods p223 Buoy, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.889667 -66.98483 17.889667 -66.98483 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.05300 28.0530 NA 7 7 28.13967 28.053 0.4414280 sum 7 summer 3.2000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.3725128 301.90 0.000000
532 EI0541 CD082 SI153 1.6000000 % 0.2 Figure 4 2004 0 0 Materials and Methods Methods p223 Buoy, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.889667 -66.98483 17.889667 -66.98483 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.18800 28.1880 NA 7 7 28.23267 28.188 0.4167989 sum 7 summer 0.8000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.2014465 301.90 1.024287
533 EI0542 CD082 SI153 5.4000000 % 1 Figure 4 2005 0 0 Materials and Methods Methods p223 Buoy, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.889667 -66.98483 17.889667 -66.98483 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.95800 28.9580 NA 7 7 28.99467 28.958 0.3315251 sum 7 summer 4.0000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.6257095 301.90 1.148554
534 EI0543 CD082 SI153 9.8000000 % 1.2 Figure 4 2006 0 0 Materials and Methods Methods p223 Buoy, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.889667 -66.98483 17.889667 -66.98483 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.52000 28.5200 NA 7 7 28.60600 28.520 0.1755787 sum 7 summer 4.8000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 1.2942810 301.90 8.489988
535 EI0544 CD082 SI153 3.4000000 % 0.6 Figure 4 2007 0 0 Materials and Methods Methods p223 Buoy, La Parguera, Puerto Rico Materials and Methods Study area p222 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.889667 -66.98483 17.889667 -66.98483 NA NA NA Prevalence extracted using MetaDigitise in R, n = 32 Atlantic Ocean 28.96000 28.9600 NA 7 7 28.88667 28.960 0.2098410 sum 7 summer 2.4000000 NA NA 0.32000 4 Materials and Methods Methods p223 NA 1 Depth intervals considered different sites Belt 32 10.0000 2.00 20.0000 Materials and Methods Methods p223 NA 10 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 “Caribbean white syndromes” and “White plague disease” grouped under WS 0.2899933 301.90 1.108576
536 EI0545 CD083 SI154 0.0000000 % 0 Figure 6b 2007 Oct Oct Figure 6b Atol das Rocas, Brazil Figure 1 XXXX Caribbean/Atlantic -3.850000 -33.81667 -3.850000 -33.81667 Methods Coral dataset p443 NA NA Prevalence extracted using MetaDigitise in R, n = 54 Atlantic Ocean 26.64500 26.6450 NA 10 10 27.64433 27.570 0.3445668 spr 4 spring 0.0000000 NA NA 0.54000 9 Table 1 NA 1 NA Belt 54 20.0000 0.50 10.0000 Methods Coral dataset p444 didn’t specify transect number so took avg of 4-8 and then multiplied by 9 sites 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1875076 301.40 0.000000
537 EI0546 CD083 SI154 6.8571430 % 1.224832 Figure 6b 2010 Mar Mar Figure 6b Atol das Rocas, Brazil Figure 1 XXXX Caribbean/Atlantic -3.850000 -33.81667 -3.850000 -33.81667 Methods Coral dataset p443 NA NA Prevalence extracted using MetaDigitise in R, n = 18 Atlantic Ocean 29.02800 29.0280 NA 3 3 27.89433 27.953 0.1870332 aut 9 fall 5.1965230 NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 20.0000 0.50 10.0000 Methods Coral dataset p444 didn’t specify transect number so took avg of 4-8 and then multiplied by 3 sites 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5971375 301.40 4.049971
538 EI0547 CD083 SI154 29.8571430 % 2.763285 Figure 6b 2010 Dec Dec Figure 6b Atol das Rocas, Brazil Figure 1 XXXX Caribbean/Atlantic -3.850000 -33.81667 -3.850000 -33.81667 Methods Coral dataset p443 NA NA Prevalence extracted using MetaDigitise in R, n = 54 Atlantic Ocean 27.68500 27.6850 NA 12 12 27.89433 27.953 0.1870332 sum 6 summer 20.3059170 NA NA 0.54000 9 Table 1 NA 1 NA Belt 54 20.0000 0.50 10.0000 Methods Coral dataset p444 didn’t specify transect number so took avg of 4-8 and then multiplied by 9 sites 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2421570 301.40 7.682823
539 EI0548 CD083 SI155 1.6549300 % 0.3358438 Figure 6d 2008 Sep Sep Figure 6d Fernando de Noronha, Brazil Figure 1 XXXX Caribbean/Atlantic -3.850000 -32.41667 -3.850000 -32.41667 Methods Coral dataset p443 NA NA Prevalence extracted using MetaDigitise in R, n = 30 Atlantic Ocean 26.83500 26.8350 NA 9 9 27.71033 27.718 0.2385930 spr 3 spring 1.8394920 NA NA 0.30000 5 Table 1 NA 1 NA Belt 30 20.0000 0.50 10.0000 Methods Coral dataset p444 didn’t specify transect number so took avg of 4-8 and then multiplied by 5 sites 2 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4142838 301.46 0.000000
540 EI0549 CD083 SI155 1.1619720 % 0.2746776 Figure 6d 2009 Nov Nov Figure 6d Fernando de Noronha, Brazil Figure 1 XXXX Caribbean/Atlantic -3.850000 -32.41667 -3.850000 -32.41667 Methods Coral dataset p443 NA NA Prevalence extracted using MetaDigitise in R, n = 30 Atlantic Ocean 26.95500 26.9550 NA 11 11 27.74100 27.773 0.4758071 spr 5 spring 1.5044710 NA NA 0.30000 5 Table 1 NA 1 NA Belt 30 20.0000 0.50 10.0000 Methods Coral dataset p444 didn’t specify transect number so took avg of 4-8 and then multiplied by 5 sites 2 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6228943 301.46 4.784301
541 EI0550 CD083 SI155 12.2711270 % 2.5528396 Figure 6d 2010 Nov Nov Figure 6d Fernando de Noronha, Brazil Figure 1 XXXX Caribbean/Atlantic -3.850000 -32.41667 -3.850000 -32.41667 Methods Coral dataset p443 NA NA Prevalence extracted using MetaDigitise in R, n = 24 Atlantic Ocean 27.52000 27.5200 NA 11 11 27.86033 27.890 0.1991643 spr 5 spring 12.5063090 NA NA 0.24000 4 Table 1 NA 1 NA Belt 24 20.0000 0.50 10.0000 Methods Coral dataset p444 didn’t specify transect number so took avg of 4-8 and then multiplied by 4 sites 2 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2571411 301.46 3.187110
542 EI0551 CD084 SI156 3.1000000 % 0.6 Results Impact of dredging on coral disease prevalence 2011 Dec Dec Methods Coral health and community composition surveys Montebello and Barrow Islands, Western Australia Figure 1 Eastern Indian Ocean Australia -20.605990 115.46835 -20.605990 115.46835 GoogleMaps Montebello and Barrow Islands NA NA NA Indian Ocean 27.63000 27.6300 NA 12 12 28.62200 28.938 0.8777522 sum 6 summer NA NA NA 0.54000 6 Methods Coral health and community composition surveys NA NA NA Belt 18 15.0000 2.00 30.0000 Methods Coral health and community composition surveys NA 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5014038 302.07 1.025704
543 EI0552 CD084 SI156 4.7000000 % 1.5 Results Impact of dredging on coral disease prevalence 2011 Dec Dec Methods Coral health and community composition surveys Montebello and Barrow Islands, Western Australia Figure 1 Eastern Indian Ocean Australia -20.605990 115.46835 -20.605990 115.46835 GoogleMaps Montebello and Barrow Islands NA NA NA Indian Ocean 27.63000 27.6300 NA 12 12 28.62200 28.938 0.8777522 sum 6 summer NA NA NA 0.27000 3 Methods Coral health and community composition surveys NA NA NA Belt 9 15.0000 2.00 30.0000 Methods Coral health and community composition surveys NA 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5014038 302.07 1.025704
544 EI0553 CD084 SI156 7.2600000 % 1.56 Results Impact of dredging on coral disease prevalence 2011 Dec Dec Methods Coral health and community composition surveys Montebello and Barrow Islands, Western Australia Figure 1 Eastern Indian Ocean Australia -20.605990 115.46835 -20.605990 115.46835 GoogleMaps Montebello and Barrow Islands NA NA NA Indian Ocean 27.63000 27.6300 NA 12 12 28.62200 28.938 0.8777522 sum 6 summer NA NA NA 0.18000 2 Methods Coral health and community composition surveys NA NA NA Belt 6 15.0000 2.00 30.0000 Methods Coral health and community composition surveys NA 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5014038 302.07 1.025704
545 EI0554 CD085 SI157 17.0000000 % NA Table 1 2001 Mar Mar Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 22.47300 22.4730 NA 3 3 27.51033 27.875 1.3669808 spr 3 spring NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 1.4271240 301.82 0.000000
546 EI0555 CD085 SI157 11.0000000 % NA Table 1 2001 Mar Mar Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 22.47300 22.4730 NA 3 3 27.51033 27.875 1.3669808 spr 3 spring NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 1.4271240 301.82 0.000000
547 EI0556 CD085 SI157 3.0000000 % NA Table 1 2001 Mar Mar Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 22.47300 22.4730 NA 3 3 27.51033 27.875 1.3669808 spr 3 spring NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 1.4271240 301.82 0.000000
548 EI0557 CD085 SI157 21.0000000 % NA Table 1 2001 Aug Aug Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 28.65800 28.6580 NA 8 8 27.51033 27.875 1.3669808 sum 8 summer NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 0.8871460 301.82 2.572831
549 EI0558 CD085 SI157 13.0000000 % NA Table 1 2001 Aug Aug Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 28.65800 28.6580 NA 8 8 27.51033 27.875 1.3669808 sum 8 summer NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 0.8871460 301.82 2.572831
550 EI0559 CD085 SI157 4.0000000 % NA Table 1 2001 Aug Aug Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 28.65800 28.6580 NA 8 8 27.51033 27.875 1.3669808 sum 8 summer NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 0.8871460 301.82 2.572831
551 EI0560 CD085 SI157 17.0000000 % NA Table 1 2002 Mar Mar Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 22.38300 22.3830 NA 3 3 27.13867 27.618 1.2282738 spr 3 spring NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 1.5621414 301.82 2.572831
552 EI0561 CD085 SI157 10.0000000 % NA Table 1 2002 Mar Mar Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 22.38300 22.3830 NA 3 3 27.13867 27.618 1.2282738 spr 3 spring NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 1.5621414 301.82 2.572831
553 EI0562 CD085 SI157 3.0000000 % NA Table 1 2002 Mar Mar Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 22.38300 22.3830 NA 3 3 27.13867 27.618 1.2282738 spr 3 spring NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 1.5621414 301.82 2.572831
554 EI0563 CD085 SI157 22.0000000 % NA Table 1 2002 Aug Aug Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 28.05500 28.0550 NA 8 8 27.13867 27.618 1.2282738 sum 8 summer NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 0.8382416 301.82 1.000008
555 EI0564 CD085 SI157 8.0000000 % NA Table 1 2002 Aug Aug Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 28.05500 28.0550 NA 8 8 27.13867 27.618 1.2282738 sum 8 summer NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 0.8382416 301.82 1.000008
556 EI0565 CD085 SI157 1.0000000 % NA Table 1 2002 Aug Aug Table 1 Gulf of Eilat, Red Sea Material and Methods Field censuses Western Indian Ocean Middle East 29.501210 34.91748 27.680610 34.60500 GoogleMaps NA NA NA Indian Ocean 28.05500 28.0550 NA 8 8 27.13867 27.618 1.2282738 sum 8 summer NA NA NA 0.18000 3 Table 1 NA 1 NA Belt 18 10.0000 1.00 10.0000 Material and Methods Field censuses NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 NA 0.8382416 301.82 1.000008
557 EI0566 CD086 SI158 5.3953490 % 1.1390603 Figure 6b 2002 Jun Jun Methods AGRRA surveys and disease monitoring p124 Coral City, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 28.63800 28.6380 NA 6 6 29.33367 29.323 0.7010611 sum 6 summer 1.1390603 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2564392 302.62 0.000000
558 EI0567 CD086 SI158 0.0000000 % 0 Figure 6b 2004 Feb Feb Methods AGRRA surveys and disease monitoring p124 Coral City, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 26.89800 26.8980 NA 2 2 29.21867 29.290 0.5286226 win 2 winter 0.0000000 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6146469 302.62 0.000000
559 EI0568 CD086 SI158 4.1860470 % 0.616991 Figure 6b 2002 Jun Jun Methods AGRRA surveys and disease monitoring p124 Grundy’s Gardens, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 28.63800 28.6380 NA 6 6 29.33367 29.323 0.7010611 sum 6 summer 0.6169910 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2564392 302.62 0.000000
560 EI0569 CD086 SI158 0.0000000 % 0 Figure 6b 2004 Feb Feb Methods AGRRA surveys and disease monitoring p124 Grundy’s Gardens, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 26.89800 26.8980 NA 2 2 29.21867 29.290 0.5286226 win 2 winter 0.0000000 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6146469 302.62 0.000000
561 EI0570 CD086 SI158 3.9069770 % 1.4475558 Figure 6b 2002 Jun Jun Methods AGRRA surveys and disease monitoring p124 Jigsaw Puzzle, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 28.63800 28.6380 NA 6 6 29.33367 29.323 0.7010611 sum 6 summer 1.4475558 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2564392 302.62 0.000000
562 EI0571 CD086 SI158 1.1627910 % 0.3796868 Figure 6b 2004 Feb Feb Methods AGRRA surveys and disease monitoring p124 Jigsaw Puzzle, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 26.89800 26.8980 NA 2 2 29.21867 29.290 0.5286226 win 2 winter 0.3796868 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6146469 302.62 0.000000
563 EI0572 CD086 SI158 5.5813950 % 0.8542952 Figure 6b 2002 Jun Jun Methods AGRRA surveys and disease monitoring p124 Sailfin, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 28.63800 28.6380 NA 6 6 29.33367 29.323 0.7010611 sum 6 summer 0.8542952 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2564392 302.62 0.000000
564 EI0573 CD086 SI158 0.7441860 % 0.403172 Figure 6b 2004 Feb Feb Methods AGRRA surveys and disease monitoring p124 Sailfin, Little Cayman, Cayman Islands Methods Measuring in the field Study site p124 Caribbean & Gulf of Mexico Caribbean/Atlantic 19.698120 -80.03659 19.698120 -80.03659 GoogleMaps Little Cayman NA NA Prevalence extracted using MetaDigitise in RStudio, n = Atlantic Ocean 26.89800 26.8980 NA 2 2 29.21867 29.290 0.5286226 win 2 winter 0.4031720 NA NA 0.01000 1 Figure 6b NA 1 NA Line 1 10.0000 1.00 10.0000 Methods AGRRA surveys and disease monitoring p125 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6146469 302.62 0.000000
565 EI0574 CD087 SI159 49.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Diploastrea Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
566 EI0575 CD087 SI159 19.6000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Fungia Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
567 EI0576 CD087 SI159 14.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Diploastrea Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
568 EI0577 CD087 SI159 32.2000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Fungia Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
569 EI0578 CD087 SI159 15.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Diploastrea Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
570 EI0579 CD087 SI159 30.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Fungia Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
571 EI0580 CD087 SI159 3.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Diploastrea Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
572 EI0581 CD087 SI159 26.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Fungia Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
573 EI0582 CD087 SI159 17.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Diploastrea Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
574 EI0583 CD087 SI159 26.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Fungia Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
575 EI0584 CD087 SI159 8.3000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Diploastrea Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
576 EI0585 CD087 SI159 24.0000000 % NA Table 3 2004 Aug Aug Materials and Methods Wakatobi Island Chain Materials and Methods Coral Triangle & SE Asia Southeast Asia -5.546940 123.93089 -5.546940 123.93089 GoogleMaps Wakatobi National Park NA NA Fungia Pacific Ocean 26.87500 26.8750 NA 8 8 29.67533 29.453 0.4816597 win 2 winter NA NA NA 0.07500 1 Table 3 NA 0 NA Belt 5 15.0000 1.00 15.0000 Materials and Methods NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4807205 302.43 2.644274
577 EI0586 CD088 SI160 13.7000000 % 0.82 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 1 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
578 EI0587 CD088 SI160 9.5000000 % 0.9 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 1 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
579 EI0588 CD088 SI160 0.6500000 % 7.00E-02 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 1 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
580 EI0589 CD088 SI160 0.2200000 % 0.04 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 1 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
581 EI0590 CD088 SI160 2.2000000 % 0.44 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 1 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
582 EI0591 CD088 SI160 2.9000000 % 0.96 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 0 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
583 EI0592 CD088 SI160 0.1400000 % 0.14 Table 1 2011 Jan-Feb Jan-Feb Table 1 Western Hawai’i Figure 1 Western Pacific Polynesia 19.571080 -155.96475 19.571080 -155.96475 GoogleMaps Keauhou Bay, HI NA NA NA Pacific Ocean 24.71250 24.7125 0.1237431 1 2 25.12933 25.055 0.3034082 win 1 winter NA NA NA 0.72000 9 Figure 1 NA 1 NA Belt 36 10.0000 2.00 20.0000 Methods Coral health assessments p3 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4599915 300.38 0.000000
585 EI0594 CD091 SI162 12.2058820 % 1.7647059 Figure 4 1997 Apr Apr Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 26.66500 26.6650 NA 4 4 27.71533 27.453 0.2275882 spr 4 spring 5.2941180 NA NA 0.54000 3 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3542786 301.47 1.048555
586 EI0595 CD091 SI163 7.5000000 % 1.0294118 Figure 4 1997 Apr Apr Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 26.66500 26.6650 NA 4 4 27.71533 27.453 0.2275882 spr 4 spring 1.7829930 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3542786 301.47 1.048555
587 EI0596 CD091 SI164 0.0000000 % 0 Figure 4 1997 Apr Apr Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 26.66500 26.6650 NA 4 4 27.71533 27.453 0.2275882 spr 4 spring 0.0000000 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9771423 301.58 1.054278
588 EI0597 CD091 SI162 15.0000000 % 4.1176471 Figure 4 1997 Jul Jul Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 27.45300 27.4530 NA 7 7 27.71533 27.453 0.2275882 sum 7 summer 12.3529410 NA NA 0.54000 3 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8321381 301.47 1.048555
589 EI0598 CD091 SI163 7.0588240 % 1.7647059 Figure 4 1997 Jul Jul Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.45300 27.4530 NA 7 7 27.71533 27.453 0.2275882 sum 7 summer 3.0565600 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8321381 301.47 1.048555
590 EI0599 CD091 SI164 4.7058820 % 0.7352941 Figure 4 1997 Jul Jul Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.45300 27.4530 NA 7 7 27.71533 27.453 0.2275882 sum 7 summer 1.2735670 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9600067 301.58 1.054278
591 EI0600 CD091 SI162 14.2647060 % 1.4705882 Figure 4 1997 Oct Oct Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 28.64500 28.6450 NA 10 10 27.71533 27.453 0.2275882 aut 10 fall 4.4117650 NA NA 0.54000 3 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5985718 301.47 1.048555
592 EI0601 CD091 SI163 9.2647060 % 2.6470588 Figure 4 1997 Oct Oct Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.64500 28.6450 NA 10 10 27.71533 27.453 0.2275882 aut 10 fall 4.5848400 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5985718 301.47 1.048555
593 EI0602 CD091 SI164 5.8823530 % 1.1764706 Figure 4 1997 Oct Oct Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.64500 28.6450 NA 10 10 27.71533 27.453 0.2275882 aut 10 fall 2.0377070 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0742798 301.58 1.054278
594 EI0603 CD091 SI162 11.1764710 % 2.5 Figure 4 1997 Dec Dec Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 27.37800 27.3780 NA 12 12 27.71533 27.453 0.2275882 win 12 winter 7.5000000 NA NA 0.54000 3 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.8157425 301.47 0.000000
595 EI0604 CD091 SI163 5.4411760 % 1.9117647 Figure 4 1997 Dec Dec Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.37800 27.3780 NA 12 12 27.71533 27.453 0.2275882 win 12 winter 3.3112740 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.8157425 301.47 0.000000
596 EI0605 CD091 SI164 3.3823530 % 1.7647059 Figure 4 1997 Dec Dec Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.37800 27.3780 NA 12 12 27.71533 27.453 0.2275882 win 12 winter 3.0565600 NA NA 0.18000 1 Methods Sampling and data analysis p620 9398 1 Coral_N aggregated for all reefs Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.6403580 301.58 0.000000
597 EI0606 CD091 SI162 11.3235290 % 1.1764706 Figure 4 1998 Feb Feb Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 26.71000 26.7100 NA 2 2 28.57300 28.253 0.3082611 win 2 winter 3.5294120 NA NA 0.54000 3 Methods Sampling and data analysis p620 NA 1 NA Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.2138977 301.47 0.000000
598 EI0607 CD091 SI163 3.8235290 % 1.3235294 Figure 4 1998 Feb Feb Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 26.71000 26.7100 NA 2 2 28.57300 28.253 0.3082611 win 2 winter 2.2924200 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.2138977 301.47 0.000000
599 EI0608 CD091 SI164 1.6176470 % 0.8823529 Figure 4 1998 Feb Feb Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 26.71000 26.7100 NA 2 2 28.57300 28.253 0.3082611 win 2 winter 1.5282800 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 2.0314178 301.58 0.000000
600 EI0609 CD091 SI162 16.6176470 % 0.8823529 Figure 4 1998 Apr Apr Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 27.19300 27.1930 NA 4 4 28.57300 28.253 0.3082611 spr 4 spring 2.6470590 NA NA 0.54000 3 Methods Sampling and data analysis p620 NA 1 NA Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7299805 301.47 0.000000
601 EI0610 CD091 SI163 6.3235290 % 2.2058824 Figure 4 1998 Apr Apr Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.19300 27.1930 NA 4 4 28.57300 28.253 0.3082611 spr 4 spring 3.8207000 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7299805 301.47 0.000000
602 EI0611 CD091 SI164 3.0882350 % 1.3235294 Figure 4 1998 Apr Apr Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.19300 27.1930 NA 4 4 28.57300 28.253 0.3082611 spr 4 spring 2.2924200 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6510696 301.58 0.000000
603 EI0612 CD091 SI162 11.7647060 % 1.4705882 Figure 4 1998 Jun Jun Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 28.59800 28.5980 NA 6 6 28.57300 28.253 0.3082611 sum 6 summer 4.4117650 NA NA 0.54000 3 Methods Sampling and data analysis p620 NA 1 NA Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7299805 301.47 0.000000
604 EI0613 CD091 SI163 5.1470590 % 2.6470588 Figure 4 1998 Jun Jun Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.59800 28.5980 NA 6 6 28.57300 28.253 0.3082611 sum 6 summer 4.5848400 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7299805 301.47 0.000000
605 EI0614 CD091 SI164 3.9705880 % 2.0588235 Figure 4 1998 Jun Jun Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.59800 28.5980 NA 6 6 28.57300 28.253 0.3082611 sum 6 summer 3.5659870 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6510696 301.58 0.000000
606 EI0615 CD091 SI162 16.6176470 % 3.2352941 Figure 4 1998 Aug Aug Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 28.86800 28.8680 NA 8 8 28.57300 28.253 0.3082611 sum 8 summer 9.7058820 NA NA 0.54000 3 Methods Sampling and data analysis p620 NA 1 NA Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1428528 301.47 0.000000
607 EI0616 CD091 SI163 8.5294120 % 1.9117647 Figure 4 1998 Aug Aug Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.86800 28.8680 NA 8 8 28.57300 28.253 0.3082611 sum 8 summer 3.3112740 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1428528 301.47 0.000000
608 EI0617 CD091 SI164 0.0000000 % 0 Figure 4 1998 Aug Aug Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.86800 28.8680 NA 8 8 28.57300 28.253 0.3082611 sum 8 summer 0.0000000 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2107086 301.58 0.000000
609 EI0618 CD091 SI162 8.0882350 % 2.2058824 Figure 4 1998 Oct Oct Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 28.93000 28.9300 NA 10 10 28.57300 28.253 0.3082611 aut 10 fall 6.6176470 NA NA 0.54000 3 Methods Sampling and data analysis p620 NA 1 NA Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4139481 301.47 4.638586
610 EI0619 CD091 SI163 1.4705880 % 1.6176471 Figure 4 1998 Oct Oct Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.93000 28.9300 NA 10 10 28.57300 28.253 0.3082611 aut 10 fall 2.8018470 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4139481 301.47 4.638586
611 EI0620 CD091 SI164 0.0000000 % 0 Figure 4 1998 Oct Oct Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 28.93000 28.9300 NA 10 10 28.57300 28.253 0.3082611 aut 10 fall 0.0000000 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4075165 301.58 5.652842
612 EI0621 CD091 SI162 7.7941180 % 0.5882353 Figure 4 1998 Dec Dec Figure 4 Gayraca, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.318730 -74.10829 11.318730 -74.10829 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 27.38000 27.3800 NA 12 12 28.57300 28.253 0.3082611 win 12 winter 1.7647060 NA NA 0.54000 3 Methods Sampling and data analysis p620 NA 1 NA Belt 9 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4139481 301.47 6.911417
613 EI0622 CD091 SI163 3.9705880 % 1.9117647 Figure 4 1998 Dec Dec Figure 4 Chengue, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.317340 -74.13297 11.317340 -74.13297 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.38000 27.3800 NA 12 12 28.57300 28.253 0.3082611 win 12 winter 3.3112740 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4139481 301.47 6.911417
614 EI0623 CD091 SI164 0.0000000 % 0 Figure 4 1998 Dec Dec Figure 4 Granate, Tayrona Natural Park, Colombia Methods Study area p619 Caribbean & Gulf of Mexico Caribbean/Atlantic 11.272050 -74.19651 11.272050 -74.19651 Methods Study area p619, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Atlantic Ocean 27.38000 27.3800 NA 12 12 28.57300 28.253 0.3082611 win 12 winter 0.0000000 NA NA 0.18000 1 Methods Sampling and data analysis p620 NA 1 NA Belt 3 30.0000 2.00 60.0000 Methods Sampling and data analysis p620 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4075165 301.58 7.907119
615 EI0624 CD092 SI165 0.9100000 % NA Table 2 2004 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.62033 27.8180 0.2285966 7 9 26.69867 27.370 1.5668571 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4249878 300.46 8.265669
616 EI0625 CD092 SI165 0.0200000 % NA Table 2 2004 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.62033 27.8180 0.2285966 7 9 26.69867 27.370 1.5668571 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4249878 300.46 8.265669
617 EI0626 CD092 SI165 0.0000000 % NA Table 2 2004 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.62033 27.8180 0.2285966 7 9 26.69867 27.370 1.5668571 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4249878 300.46 8.265669
618 EI0627 CD092 SI165 0.5900000 % NA Table 2 2004 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.62033 27.8180 0.2285966 7 9 26.69867 27.370 1.5668571 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4249878 300.46 8.265669
619 EI0628 CD092 SI165 0.0000000 % NA Table 2 2004 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.62033 27.8180 0.2285966 7 9 26.69867 27.370 1.5668571 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4249878 300.46 8.265669
620 EI0629 CD092 SI165 0.5900000 % NA Table 2 2005 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.50100 27.8650 0.3465663 7 9 26.52200 27.463 1.9881887 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7532272 300.46 2.252853
621 EI0630 CD092 SI165 0.0000000 % NA Table 2 2005 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.50100 27.8650 0.3465663 7 9 26.52200 27.463 1.9881887 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7532272 300.46 2.252853
622 EI0631 CD092 SI165 0.0500000 % NA Table 2 2005 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.50100 27.8650 0.3465663 7 9 26.52200 27.463 1.9881887 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7532272 300.46 2.252853
623 EI0632 CD092 SI165 0.5000000 % NA Table 2 2005 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.50100 27.8650 0.3465663 7 9 26.52200 27.463 1.9881887 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7532272 300.46 2.252853
624 EI0633 CD092 SI165 0.0100000 % NA Table 2 2005 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.50100 27.8650 0.3465663 7 9 26.52200 27.463 1.9881887 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7532272 300.46 2.252853
625 EI0634 CD092 SI165 0.3500000 % NA Table 2 2006 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.30267 27.7750 0.4094713 7 9 26.38933 27.048 1.8073755 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9100037 300.46 3.435676
626 EI0635 CD092 SI165 0.0100000 % NA Table 2 2006 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.30267 27.7750 0.4094713 7 9 26.38933 27.048 1.8073755 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9100037 300.46 3.435676
627 EI0636 CD092 SI165 0.0900000 % NA Table 2 2006 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.30267 27.7750 0.4094713 7 9 26.38933 27.048 1.8073755 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9100037 300.46 3.435676
628 EI0637 CD092 SI165 0.2100000 % NA Table 2 2006 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.30267 27.7750 0.4094713 7 9 26.38933 27.048 1.8073755 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9100037 300.46 3.435676
629 EI0638 CD092 SI165 0.0000000 % NA Table 2 2006 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.30267 27.7750 0.4094713 7 9 26.38933 27.048 1.8073755 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9100037 300.46 3.435676
630 EI0639 CD092 SI165 0.2300000 % NA Table 2 2007 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.11100 27.6300 0.6107394 7 9 25.74200 26.438 2.3158171 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7542877 300.46 1.007133
631 EI0640 CD092 SI165 0.0100000 % NA Table 2 2007 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.11100 27.6300 0.6107394 7 9 25.74200 26.438 2.3158171 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7542877 300.46 1.007133
632 EI0641 CD092 SI165 0.0000000 % NA Table 2 2007 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.11100 27.6300 0.6107394 7 9 25.74200 26.438 2.3158171 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7542877 300.46 1.007133
633 EI0642 CD092 SI165 0.2200000 % NA Table 2 2007 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.11100 27.6300 0.6107394 7 9 25.74200 26.438 2.3158171 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7542877 300.46 1.007133
634 EI0643 CD092 SI165 0.1500000 % NA Table 2 2008 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.67600 27.7180 0.2823523 7 9 26.48767 27.375 1.8419546 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7457123 300.46 0.000000
635 EI0644 CD092 SI165 0.0000000 % NA Table 2 2008 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.67600 27.7180 0.2823523 7 9 26.48767 27.375 1.8419546 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7457123 300.46 0.000000
636 EI0645 CD092 SI165 0.0000000 % NA Table 2 2008 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.67600 27.7180 0.2823523 7 9 26.48767 27.375 1.8419546 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7457123 300.46 0.000000
637 EI0646 CD092 SI165 0.1100000 % NA Table 2 2008 Jul-Sep Jul-Sep Table 2 Bermuda Materials and Methods Spatial patterns of coral cover and disease prevalence p81 Caribbean & Gulf of Mexico Caribbean/Atlantic 32.305060 -64.73344 32.305060 -64.73344 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 27.67600 27.7180 0.2823523 7 9 26.48767 27.375 1.8419546 sum 7 summer NA NA NA 7.80000 26 Figure 1 160000 0 Coral_N aggregated for whole study Belt 130 30.0000 2.00 60.0000 Materials and Methods Spatial patterns of coral cover and disease prevalence p81-82 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7457123 300.46 0.000000
638 EI0647 CD093 SI166 2.0000000 % NA Table 2 1996 0 0 Table 2 Triangulos Este, Campeche Bank Gulf of Mexico Materials and Methods Study sites p4 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.860450 -92.53477 21.860450 -92.53477 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 28.72500 28.7250 NA 7 7 28.71267 28.725 0.2237553 sum 7 summer NA NA NA 0.07200 3 Materials and Methods Method p5 86 0 NA Belt 6 20.0000 0.60 12.0000 Materials and Methods Methods p5 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4149933 302.25 2.615693
639 EI0648 CD093 SI166 37.0000000 % NA Table 2 2001 0 0 Table 2 Triangulos Este, Campeche Bank Gulf of Mexico Materials and Methods Study sites p4 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.860450 -92.53477 21.860450 -92.53477 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 28.70500 28.7050 NA 7 7 28.73767 28.705 0.7815123 sum 7 summer NA NA NA 0.15600 6 Materials and Methods Method p5 533 0 NA Belt 39 10.0000 0.40 4.0000 Materials and Methods Methods p5 NA 3 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5099945 302.25 1.014283
640 EI0649 CD093 SI167 4.0000000 % NA Table 2 1996 0 0 Table 2 Cayos Arcas, Campeche Bank Gulf of Mexico Materials and Methods Study sites p4 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.159020 -91.95568 21.159020 -91.95568 GoogleMaps Cayos Arcas NA NA NA Atlantic Ocean 28.58800 28.5880 NA 7 7 28.57467 28.588 0.1953413 sum 7 summer NA NA NA 0.07200 3 Materials and Methods Method p5 153 0 NA Belt 6 20.0000 0.60 12.0000 Materials and Methods Methods p5 NA 2 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2789230 302.00 3.661409
641 EI0650 CD093 SI167 34.0000000 % NA Table 2 2001 0 0 Table 2 Cayos Arcas, Campeche Bank Gulf of Mexico Materials and Methods Study sites p4 Caribbean & Gulf of Mexico Caribbean/Atlantic 21.159020 -91.95568 21.159020 -91.95568 GoogleMaps Cayos Arcas NA NA NA Atlantic Ocean 28.57000 28.5700 NA 7 7 28.60600 28.570 0.7266699 sum 7 summer NA NA NA 0.15600 17 Materials and Methods Method p5 253 0 NA Belt 42 10.0000 0.40 4.0000 Materials and Methods Methods p5 NA 2 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3028564 302.00 0.000000
642 EI0651 CD093 SI168 0.0000000 % NA Table 2 1998 0 0 Table 2 Puerto Morelos, Yucatan Peninsula Materials and Methods Study sites p4 Caribbean & Gulf of Mexico Caribbean/Atlantic 20.849220 -86.87585 20.849220 -86.87585 GoogleMaps Puerto Morelo NA NA NA Atlantic Ocean 29.23500 29.2350 NA 7 7 29.32700 29.235 0.4176696 sum 7 summer NA NA NA 0.30000 3 Materials and Methods Method p5 212 0 NA Quadrat 3 50.0000 2.00 100.0000 Materials and Methods Methods p5 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3585587 301.85 0.000000
643 EI0652 CD093 SI168 26.0000000 % NA Table 2 2001 0 0 Table 2 Puerto Morelos, Yucatan Peninsula Materials and Methods Study sites p4 Caribbean & Gulf of Mexico Caribbean/Atlantic 20.849220 -86.87585 20.849220 -86.87585 GoogleMaps Puerto Morelo NA NA NA Atlantic Ocean 28.69000 28.6900 NA 7 7 28.69933 28.690 0.4890662 sum 7 summer NA NA NA 0.94200 3 Materials and Methods Method p5 68 0 NA Circle 3 10.0000 NA 314.0000 Materials and Methods Methods p5 length is diameter 2 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6789703 301.85 0.000000
644 EI0653 CD094 SI169 2.4000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Lalaan, San Jose, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 466 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
645 EI0654 CD094 SI169 2.4000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Calo River, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 124 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
646 EI0655 CD094 SI169 2.7000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Cangmating, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 333 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
647 EI0656 CD094 SI169 1.2000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Maslog River, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 335 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
648 EI0657 CD094 SI169 18.5000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Caloncalong Pt, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 363 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
649 EI0658 CD094 SI169 14.3000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Agan-an North, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 582 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
650 EI0659 CD094 SI169 5.3000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Agan-an South, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 397 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
651 EI0660 CD094 SI169 4.7000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Airport Runway, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 322 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
652 EI0661 CD094 SI169 29.4000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Piapi, Escano, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 248 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
653 EI0662 CD094 SI169 43.6000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Looc, Dumaguete City Pier, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 351 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
654 EI0663 CD094 SI169 0.0000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Mangnao, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 252 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
655 EI0664 CD094 SI169 0.3000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Banilad, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 670 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
656 EI0665 CD094 SI169 1.8000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Poblacion, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 954 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
657 EI0666 CD094 SI169 0.0000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Masaplod, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 190 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
658 EI0667 CD094 SI169 0.4000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Bonbonan, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 256 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
659 EI0668 CD094 SI169 0.9000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Sillon Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 551 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
660 EI0669 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Panagsama, Cebu Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 872 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
661 EI0670 CD094 SI169 0.2000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Pescador Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 611 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
662 EI0671 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Calagcalag, Negros Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 301 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
663 EI0672 CD094 SI169 5.7000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Bato Cebu Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 351 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
664 EI0673 CD094 SI169 53.7000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Looc, Cebu Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 67 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
665 EI0674 CD094 SI169 11.5000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Sumilon Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 288 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
666 EI0675 CD094 SI169 0.6000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Alona, Panglao Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 311 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
667 EI0676 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Tubod, Siquijor Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 335 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
668 EI0677 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Olango Island, Cebu Strait Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 243 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
669 EI0678 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Snake Island, Honda Bay, Palawan Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 223 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
670 EI0679 CD094 SI169 4.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Sabang Bay, Palawan Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 149 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
671 EI0680 CD094 SI169 15.3000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Lalaan, San Jose, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 380 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
672 EI0681 CD094 SI169 12.3000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Calo River, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 122 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
673 EI0682 CD094 SI169 25.2000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Cangmating, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 230 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
674 EI0683 CD094 SI169 23.1000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Maslog River, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 269 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
675 EI0684 CD094 SI169 32.8000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Caloncalong Pt, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 314 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
676 EI0685 CD094 SI169 39.1000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Agan-an North, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 432 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
677 EI0686 CD094 SI169 38.5000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Agan-an South, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 392 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
678 EI0687 CD094 SI169 19.5000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Airport Runway, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 298 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
679 EI0688 CD094 SI169 2.2000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Piapi, Escano, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 229 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
680 EI0689 CD094 SI169 6.2000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Looc, Dumaguete City Pier, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 291 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
681 EI0690 CD094 SI169 0.4000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Mangnao, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 238 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
682 EI0691 CD094 SI169 4.4000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Banilad, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 496 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
683 EI0692 CD094 SI169 9.2000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Poblacion, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 328 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
684 EI0693 CD094 SI169 0.9000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Masaplod, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 110 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
685 EI0694 CD094 SI169 1.8000000 % NA Table 1 2003 Mar-Apr Mar-Apr Materials and Methods Gradient study p11 Bonbonan, Negros Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 27.97500 27.9750 0.9333821 3 4 29.55200 29.570 0.3053984 spr 3 spring NA NA NA 0.12000 1 Table 1 223 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
686 EI0695 CD094 SI169 15.9000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Sillon Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 383 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
687 EI0696 CD094 SI169 1.6000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Panagsama, Cebu Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 701 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
688 EI0697 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Pescador Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 426 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
689 EI0698 CD094 SI169 11.2000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Calagcalag, Negros Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 196 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
690 EI0699 CD094 SI169 4.9000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Bato Cebu Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 325 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
691 EI0700 CD094 SI169 3.9000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Looc, Cebu Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 52 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
692 EI0701 CD094 SI169 0.7000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Sumilon Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 136 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
693 EI0702 CD094 SI169 1.7000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Alona, Panglao Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 121 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
694 EI0703 CD094 SI169 0.9000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Tubod, Siquijor Island Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 222 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
695 EI0704 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Olango Island, Cebu Strait Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 225 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
696 EI0705 CD094 SI169 5.8000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Snake Island, Honda Bay, Palawan Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 104 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
697 EI0706 CD094 SI169 0.0000000 % NA Table 1 2003 Apr-May Apr-May Materials and Methods Region-wide study p11 Sabang Bay, Palawan Table 1 Coral Triangle & SE Asia Southeast Asia 11.000000 122.37939 11.003200 123.50017 GoogleMaps Visayas NA NA NA Pacific Ocean 29.09750 29.0975 0.6540732 4 5 29.55200 29.570 0.3053984 spr 4 spring NA NA NA 0.12000 1 Table 1 149 1 Mixed porites sp Belt 6 10.0000 2.00 20.0000 Materials and Methods Transect methodology p10 NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4542389 302.67 0.000000
698 EI0707 CD095 SI170 3.7000000 % NA Table 1 2001 Jun-Aug Jun-Aug Methods and Materials Survey and analytical techniques p127 Butler Bay Table 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.255000 -62.52000 18.255000 -62.52000 Methods and Materials Study sites p126 NA NA NA Atlantic Ocean 28.25367 28.2030 0.3338951 6 8 28.25367 28.203 0.3338951 sum 6 summer NA NA NA 0.30200 1 Table 1 1344 1 NA Belt 7 20.0000 2.00 40.0000 Methods and Materials Survey and analytical methods p127 Transect length varied per transect due to environmental challenges, 20m on average; Study area metric in Moderator Data accurate to real study area size 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8007202 301.38 0.000000
699 EI0708 CD095 SI171 13.6000000 % NA Table 1 2001 Jun-Aug Jun-Aug Methods and Materials Survey and analytical techniques p127 Frederiksted Table 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.233330 -65.45833 18.233330 -65.45833 Methods and Materials Study sites p126 NA NA NA Atlantic Ocean 28.32800 28.2780 0.3477070 6 8 28.32800 28.278 0.3477070 sum 6 summer NA NA NA 0.34300 1 Table 1 566 1 NA Belt 7 20.0000 2.00 40.0000 Methods and Materials Survey and analytical methods p127 Transect length varied per transect due to environmental challenges, 20m on average; Study area metric in Moderator Data accurate to real study area size 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7428436 301.68 0.000000
700 EI0709 CD096 SI172 64.7000000 % NA Results Prevalence and severity of Gas 2010 Sep-Nov Sep-Nov Materials and Methods Study site and sample collection Hardy Reef, Great Barrier Reef Marine Park Materials and Methods Study site and sample collection Western Pacific Australia -19.766667 149.25000 -19.766667 149.25000 Materials and Methods Study site and sample collection, GoogleMaps NA NA near unused tourist platform Pacific Ocean 25.62033 25.6880 1.0651137 9 11 28.14100 28.305 0.1443891 spr 3 spring NA NA NA 0.03000 1 Materials and Methods Study site and sample collection 419 0 NA Belt 1 15.0000 2.00 30.0000 Materials and Methods Prevalence and severity of Gas NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7885742 301.30 0.000000
701 EI0710 CD096 SI172 68.8000000 % NA Results Prevalence and severity of Gas 2011 Jul Jul Materials and Methods Study site and sample collection Hardy Reef, Great Barrier Reef Marine Park Materials and Methods Study site and sample collection Western Pacific Australia -19.766667 149.25000 -19.766667 149.25000 Materials and Methods Study site and sample collection, GoogleMaps NA NA near unused tourist platform Pacific Ocean 22.40500 22.4050 NA 7 7 28.06433 27.915 0.4194361 win 1 winter NA NA NA 0.03000 1 Materials and Methods Study site and sample collection 336 0 NA Belt 1 15.0000 2.00 30.0000 Materials and Methods Prevalence and severity of Gas NA 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9978333 301.30 0.000000
702 EI0711 CD097 SI173 2.3000000 % NA Table 2 2004 May-Jun May-Jun Results and Discussion p240 Buck Island Reef National Monument Materials and Methods p240 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.788090 -64.62078 17.788090 -64.62078 GoogleMaps Buck Island Reef National Monument NA NA Branching coral dominated Atlantic Ocean 27.48900 27.4890 0.5388148 5 6 28.27267 28.213 0.4355757 spr 5 spring NA NA NA 114.00000 456 Results and Discussion p240 1492 0 Only included sites that had A palmata Belt 1 25.0000 10.00 250.0000 Materials and Methods p240 NA 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6646042 301.66 0.000000
703 EI0712 CD097 SI173 3.4000000 % NA Table 2 2004 May-Jun May-Jun Results and Discussion p240 Buck Island Reef National Monument Materials and Methods p240 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.788090 -64.62078 17.788090 -64.62078 GoogleMaps Buck Island Reef National Monument NA NA Other hardbottom Atlantic Ocean 27.48900 27.4890 0.5388148 5 6 28.27267 28.213 0.4355757 spr 5 spring NA NA NA 114.00000 456 Results and Discussion p240 1000 0 Only included sites that had A palmata Belt 1 25.0000 10.00 250.0000 Materials and Methods p240 NA 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6646042 301.66 0.000000
704 EI0713 CD099 SI174 0.8000000 % NA Figure 3 2011 May May Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 27.30300 27.3030 NA 5 5 29.76600 30.025 0.9386921 spr 5 spring NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.9346466 302.77 3.815673
705 EI0714 CD099 SI174 1.2000000 % NA Figure 3 2011 May May Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 27.30300 27.3030 NA 5 5 29.76600 30.025 0.9386921 spr 5 spring NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.9346466 302.77 3.815673
706 EI0715 CD099 SI174 7.2000000 % NA Figure 3 2011 Jun Jun Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 28.72500 28.7250 NA 6 6 29.76600 30.025 0.9386921 sum 6 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.7064133 302.77 3.815673
707 EI0716 CD099 SI174 5.6000000 % NA Figure 3 2011 Jul Jul Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.02500 30.0250 NA 7 7 29.76600 30.025 0.9386921 sum 7 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.4257050 302.77 2.498545
708 EI0717 CD099 SI174 2.8000000 % NA Figure 3 2011 Jul Jul Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.02500 30.0250 NA 7 7 29.76600 30.025 0.9386921 sum 7 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.4257050 302.77 2.498545
709 EI0718 CD099 SI174 1.2000000 % NA Figure 3 2011 Jul Jul Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.02500 30.0250 NA 7 7 29.76600 30.025 0.9386921 sum 7 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.4257050 302.77 2.498545
710 EI0719 CD099 SI174 4.0000000 % NA Figure 3 2011 Sep Sep Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.01300 30.0130 NA 9 9 29.76600 30.025 0.9386921 aut 9 fall NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.4646606 302.77 4.991414
711 EI0720 CD099 SI174 2.8000000 % NA Figure 3 2011 Sep Sep Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.01300 30.0130 NA 9 9 29.76600 30.025 0.9386921 aut 9 fall NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.4646606 302.77 4.991414
712 EI0721 CD099 SI174 3.2000000 % NA Figure 3 2011 Nov Nov Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 26.72800 26.7280 NA 11 11 29.76600 30.025 0.9386921 aut 11 fall NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.7092896 302.77 6.132822
713 EI0722 CD099 SI174 7.6000000 % NA Figure 3 2012 May May Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 26.95800 26.9580 NA 5 5 29.36267 29.610 0.8581618 spr 5 spring NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.3092804 302.77 6.132822
714 EI0723 CD099 SI174 19.2000000 % NA Figure 3 2012 Jun Jun Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 28.40800 28.4080 NA 6 6 29.36267 29.610 0.8581618 sum 6 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.1399689 302.77 6.132822
715 EI0724 CD099 SI174 17.2000000 % NA Figure 3 2012 Jul Jul Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 29.61000 29.6100 NA 7 7 29.36267 29.610 0.8581618 sum 7 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 1.1399689 302.77 6.132822
716 EI0725 CD099 SI174 13.6000000 % NA Figure 3 2012 Aug Aug Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.07000 30.0700 NA 8 8 29.36267 29.610 0.8581618 sum 8 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.8317795 302.77 3.472839
717 EI0726 CD099 SI174 14.8000000 % NA Figure 3 2012 Aug Aug Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 30.07000 30.0700 NA 8 8 29.36267 29.610 0.8581618 sum 8 summer NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.8317795 302.77 3.472839
718 EI0727 CD099 SI174 36.0000000 % NA Figure 3 2012 Sep Sep Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 29.71800 29.7180 NA 9 9 29.36267 29.610 0.8581618 aut 9 fall NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.5442810 302.77 1.141408
719 EI0728 CD099 SI174 28.4000000 % NA Figure 3 2012 Nov Nov Figure 3 Upper Florida Keys National Marine Sanctuary Materials and Methods Study sites Caribbean & Gulf of Mexico Caribbean/Atlantic 24.973611 -80.45856 24.973611 -80.45856 Table 1, averaged across three wild sites NA NA Prevalence extacted using MetaDigitise in Rstudio, n = 3; wild sites Atlantic Ocean 26.22000 26.2200 NA 11 11 29.36267 29.610 0.8581618 aut 11 fall NA NA NA 0.70791 3 Table 1 NA 0 NA Circle 1 8.6667 NA 235.9700 Materials and Methods Surveillance radius averaged from 3 sizes 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Previously characterised as “WBD” but paper claims this is innacurate and more generall describe it as TL 0.5964203 302.77 0.000000
720 EI0729 CD098 SI175 2.9000000 % 1.5 Results and Discussion p98 2002 0 0 Materials and Methods p98 Navassa Materials and Methods p97 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.409420 -75.02177 18.409420 -75.02177 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 28.94300 28.9430 NA 7 7 28.96033 28.943 0.5362104 sum 7 summer NA NA NA 0.01400 1 Figure 1 NA 1 NA Quadrat 14 1.0000 1.00 1.0000 Materials and Methods p98 NA 3 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7442932 302.11 1.115709
721 EI0730 CD098 SI175 16.8000000 % 4.3 Results and Discussion p98 2004 0 0 Materials and Methods p98 Navassa Materials and Methods p97 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.409420 -75.02177 18.409420 -75.02177 Figure 1, GoogleMaps NA NA NA Atlantic Ocean 29.05300 29.0530 NA 7 7 29.00300 29.053 0.3725250 sum 7 summer NA NA NA 0.01400 1 Figure 1 NA 1 NA Quadrat 14 1.0000 1.00 1.0000 Materials and Methods p98 NA 3 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2857056 302.11 2.314272
722 EI0731 CD100 SI176 0.3400000 % 0.13, 0.74 Table 2 2010 Oct-Nov Oct-Nov Materials and Methods p160 Magoodhoo Island, Faafu Atoll, Republic of Maldives Materials and Methods p160 Western Indian Ocean Central Indian 3.066667 72.95000 3.066667 72.95000 Materials and Methods p160, GoogleMaps NA NA error in LL and UL 95% bootstrap CI Indian Ocean 28.57650 28.5765 0.0544470 10 11 29.37700 29.365 0.3551516 aut 10 fall NA NA NA 0.60000 8 Materials and Methods p160 2761 1 Coral_N aggregated from whole study Belt 24 25.0000 1.00 25.0000 Materials and Methods p160 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5685730 302.67 0.000000
723 EI0732 CD100 SI176 0.7000000 % 0.41, 0.99 Table 2 2010 Oct-Nov Oct-Nov Materials and Methods p160 Magoodhoo Island, Faafu Atoll, Republic of Maldives Materials and Methods p160 Western Indian Ocean Central Indian 3.066667 72.95000 3.066667 72.95000 Materials and Methods p160, GoogleMaps NA NA error in LL and UL 95% bootstrap CI Indian Ocean 28.57650 28.5765 0.0544470 10 11 29.37700 29.365 0.3551516 aut 10 fall NA NA NA 0.60000 8 Materials and Methods p160 2761 1 Coral_N aggregated from whole study Belt 24 25.0000 1.00 25.0000 Materials and Methods p160 NA 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5685730 302.67 0.000000
724 EI0733 CD100 SI176 0.1800000 % 0.07, 0.31 Table 2 2010 Oct-Nov Oct-Nov Materials and Methods p160 Magoodhoo Island, Faafu Atoll, Republic of Maldives Materials and Methods p160 Western Indian Ocean Central Indian 3.066667 72.95000 3.066667 72.95000 Materials and Methods p160, GoogleMaps NA NA error in LL and UL 95% bootstrap CI Indian Ocean 28.57650 28.5765 0.0544470 10 11 29.37700 29.365 0.3551516 aut 10 fall NA NA NA 0.60000 8 Materials and Methods p160 2761 1 Coral_N aggregated from whole study Belt 24 25.0000 1.00 25.0000 Materials and Methods p160 NA 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5685730 302.67 0.000000
725 EI0734 CD100 SI176 0.6400000 % 0.41, 0.92 Table 2 2010 Oct-Nov Oct-Nov Materials and Methods p160 Magoodhoo Island, Faafu Atoll, Republic of Maldives Materials and Methods p160 Western Indian Ocean Central Indian 3.066667 72.95000 3.066667 72.95000 Materials and Methods p160, GoogleMaps NA NA error in LL and UL 95% bootstrap CI Indian Ocean 28.57650 28.5765 0.0544470 10 11 29.37700 29.365 0.3551516 aut 10 fall NA NA NA 0.60000 8 Materials and Methods p160 2761 1 Coral_N aggregated from whole study Belt 24 25.0000 1.00 25.0000 Materials and Methods p160 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5685730 302.67 0.000000
726 EI0735 CD101 SI177 40.4900000 % 2.12 Results and Discussion White syndrome prevalence p5 2015 May May Material and Methods Study site p2 Water Discharge Barat, Paiton Power Plant, Probolinggo Figure 1 Coral Triangle & SE Asia Southeast Asia 7.714317 113.59846 7.714317 113.59846 Table 1, GoogleMaps NA NA Assumed error is SE Pacific Ocean 30.57000 30.5700 NA 5 5 30.18867 29.783 0.3977517 spr 5 spring NA NA NA 0.08000 1 Figure 1 NA 1 NA Belt 4 20.0000 1.00 20.0000 Material and Methods Observation Method p2 NA 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3082047 302.66 2.211404
727 EI0736 CD101 SI178 13.5300000 % 11.5 Results and Discussion White syndrome prevalence p5 2015 May May Material and Methods Study site p2 Water Discharge Timur, Paiton Power Plant, Probolinggo Figure 1 Coral Triangle & SE Asia Southeast Asia 7.715828 113.59833 7.715828 113.59833 Table 1, GoogleMaps NA NA Assumed error is SE Pacific Ocean 30.57000 30.5700 NA 5 5 30.18867 29.783 0.3977517 spr 5 spring NA NA NA 0.08000 1 Figure 1 NA 1 NA Belt 4 20.0000 1.00 20.0000 Material and Methods Observation Method p2 NA 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3082047 302.66 2.211404
728 EI0737 CD101 SI179 6.4400000 % 3.6 Results and Discussion White syndrome prevalence p5 2015 May May Material and Methods Study site p2 Water Intake, Paiton Power Plant, Probolinggo Figure 1 Coral Triangle & SE Asia Southeast Asia 7.711750 113.58756 7.711750 113.58756 Table 1, GoogleMaps NA NA Assumed error is SE Pacific Ocean 30.57000 30.5700 NA 5 5 30.18867 29.783 0.3977517 spr 5 spring NA NA NA 0.08000 1 Figure 1 NA 1 NA Belt 4 20.0000 1.00 20.0000 Material and Methods Observation Method p2 NA 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3082047 302.66 2.211404
729 EI0804 CD103 SI180 2.3000000 % NA Results p261 2005 Dec-Feb Dec-Feb Results p261 Great Barrier Reef Figure 2 Western Pacific Australia -16.824100 146.01516 -16.824100 146.01516 GoogleMaps Cairns coast, Figure 2 approximate middle point NA NA NA Pacific Ocean 28.87700 28.9480 0.1119065 12 2 28.87700 28.948 0.1119065 aut 6 summer NA NA NA 4.32000 18 Results p261 96148 1 NA Belt 108 20.0000 2.00 40.0000 Materials and Methods p259 NA 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4110870 301.89 0.000000
730 EI0805 CD103 SI180 1.2000000 % NA Results p261 2006 Dec-Feb Dec-Feb Results p261 Great Barrier Reef Figure 2 Western Pacific Australia -16.824100 146.01516 -16.824100 146.01516 GoogleMaps Cairns coast, Figure 2 approximate middle point NA NA NA Pacific Ocean 27.99300 28.3880 0.8084396 12 2 27.99300 28.388 0.8084396 aut 6 summer NA NA NA 4.32000 18 Results p261 91552 1 NA Belt 108 20.0000 2.00 40.0000 Materials and Methods p259 NA 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4164429 301.89 2.390015
731 EI0807 CD105 SI181 0.0672646 % 0.31390135 Figure 2 2006 Sep Sep Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 23.77500 23.7750 NA 9 9 27.86967 28.253 0.7165430 spr 3 spring 0.5436931 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6607056 301.89 0.000000
732 EI0808 CD105 SI181 0.5156951 % 0.44843049 Figure 2 2006 Oct Oct Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 24.80800 24.8080 NA 10 10 27.86967 28.253 0.7165430 spr 4 spring 0.7767044 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6607056 301.89 0.000000
733 EI0809 CD105 SI181 3.1838565 % 2.17488789 Figure 2 2006 Dec Dec Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 27.04300 27.0430 NA 12 12 27.86967 28.253 0.7165430 sum 6 summer 3.7670163 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3724976 301.89 0.000000
734 EI0810 CD105 SI181 9.6636771 % 5.04484305 Figure 2 2007 Feb Feb Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.31300 28.3130 NA 2 2 28.65267 28.798 0.2448296 sum 8 summer 8.7379245 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4025040 301.89 0.000000
735 EI0811 CD105 SI181 0.5381166 % 0.51569507 Figure 2 2007 Apr Apr Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 26.56300 26.5630 NA 4 4 28.65267 28.798 0.2448296 aut 10 fall 0.8932101 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2185669 301.89 0.000000
736 EI0812 CD105 SI181 0.6950673 % 0.40358744 Figure 2 2007 May May Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 25.29500 25.2950 NA 5 5 28.65267 28.798 0.2448296 aut 11 fall 0.9885833 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2185669 301.89 0.000000
737 EI0813 CD105 SI181 0.4484305 % 0.22421525 Figure 2 2007 Jul Jul Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 22.73800 22.7380 NA 7 7 28.65267 28.798 0.2448296 win 1 winter 0.5492129 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5685730 301.89 0.000000
738 EI0814 CD105 SI181 0.4708520 % 0.22421525 Figure 2 2007 Sep Sep Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 23.82500 23.8250 NA 9 9 28.65267 28.798 0.2448296 spr 3 spring 0.5492129 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0971375 301.89 0.000000
739 EI0815 CD105 SI181 1.8385650 % 0.78475336 Figure 2 2007 Nov Nov Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 27.05800 27.0580 NA 11 11 28.65267 28.798 0.2448296 spr 5 spring 1.9222453 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4514160 301.89 0.000000
740 EI0816 CD105 SI181 3.2286996 % 0.85201794 Figure 2 2008 Jan Jan Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.79800 28.7980 NA 1 1 28.48367 28.553 0.1157854 sum 7 summer 2.0870092 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1142578 301.89 0.000000
741 EI0817 CD105 SI181 3.0269058 % 0.87443946 Figure 2 2008 Feb Feb Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.79000 28.7900 NA 2 2 28.48367 28.553 0.1157854 sum 8 summer 2.1419305 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4410629 301.89 0.000000
742 EI0818 CD105 SI181 0.1793722 % 6.73E-02 Figure 2 2008 Mar Mar Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 27.65000 27.6500 NA 3 3 28.48367 28.553 0.1157854 aut 9 fall 0.1647639 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4410629 301.89 0.000000
743 EI0819 CD105 SI181 0.3587444 % 0.2690583 Figure 2 2008 Apr Apr Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 26.38800 26.3880 NA 4 4 28.48367 28.553 0.1157854 aut 10 fall 0.4660226 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4410629 301.89 0.000000
744 EI0820 CD105 SI181 0.1345291 % 0.15695067 Figure 2 2008 Aug Aug Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 22.66000 22.6600 NA 8 8 28.48367 28.553 0.1157854 win 2 winter 0.3844491 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9142761 301.89 0.000000
745 EI0821 CD105 SI181 0.7174888 % 0.20179372 Figure 2 2008 Nov Nov Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 27.29300 27.2930 NA 11 11 28.48367 28.553 0.1157854 spr 5 spring 0.4942917 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6057129 301.89 0.000000
746 EI0822 CD105 SI181 1.9955157 % 0.42600897 Figure 2 2008 Dec Dec Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.54800 28.5480 NA 12 12 28.48367 28.553 0.1157854 sum 6 summer 1.0435046 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6057129 301.89 0.000000
747 EI0823 CD105 SI181 3.2062780 % 0.82959641 Figure 2 2009 Jan Jan Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.55300 28.5530 NA 1 1 28.42467 28.313 0.4285533 sum 7 summer 2.0320879 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4207001 301.89 0.000000
748 EI0824 CD105 SI181 0.4035874 % 0.35874439 Figure 2 2006 Sep Sep Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 23.77500 23.7750 NA 9 9 27.86967 28.253 0.7165430 spr 3 spring 0.6213635 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6607056 301.89 0.000000
749 EI0825 CD105 SI181 4.2376682 % 0.98654709 Figure 2 2006 Oct Oct Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 24.80800 24.8080 NA 10 10 27.86967 28.253 0.7165430 spr 4 spring 1.7087497 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6607056 301.89 0.000000
750 EI0826 CD105 SI181 9.5515695 % 3.29596413 Figure 2 2006 Dec Dec Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 27.04300 27.0430 NA 12 12 27.86967 28.253 0.7165430 sum 6 summer 5.7087773 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3724976 301.89 0.000000
751 EI0827 CD105 SI181 9.5291480 % 4.61883408 Figure 2 2007 Feb Feb Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.31300 28.3130 NA 2 2 28.65267 28.798 0.2448296 sum 8 summer 8.0000553 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4025040 301.89 0.000000
752 EI0828 CD105 SI181 0.8744395 % 0.29147982 Figure 2 2007 Apr Apr Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 26.56300 26.5630 NA 4 4 28.65267 28.798 0.2448296 aut 10 fall 0.5048579 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2185669 301.89 0.000000
753 EI0829 CD105 SI181 0.9192825 % 0.38116592 Figure 2 2007 May May Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 25.29500 25.2950 NA 5 5 28.65267 28.798 0.2448296 aut 11 fall 0.9336620 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.2185669 301.89 0.000000
754 EI0830 CD105 SI181 0.9192825 % 0.38116592 Figure 2 2007 Jul Jul Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 22.73800 22.7380 NA 7 7 28.65267 28.798 0.2448296 win 1 winter 0.9336620 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5685730 301.89 0.000000
755 EI0831 CD105 SI181 2.7578475 % 0.85201794 Figure 2 2007 Sep Sep Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 23.82500 23.8250 NA 9 9 28.65267 28.798 0.2448296 spr 3 spring 2.0870092 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0971375 301.89 0.000000
756 EI0832 CD105 SI181 3.2062780 % 0.65022422 Figure 2 2007 Nov Nov Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 27.05800 27.0580 NA 11 11 28.65267 28.798 0.2448296 spr 5 spring 1.5927175 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4514160 301.89 0.000000
757 EI0833 CD105 SI181 1.1883408 % 0.82959641 Figure 2 2008 Jan Jan Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.79800 28.7980 NA 1 1 28.48367 28.553 0.1157854 sum 7 summer 2.0320879 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1142578 301.89 0.000000
758 EI0834 CD105 SI181 0.8520179 % 0.56053812 Figure 2 2008 Feb Feb Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.79000 28.7900 NA 2 2 28.48367 28.553 0.1157854 sum 8 summer 1.3730324 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4410629 301.89 0.000000
759 EI0835 CD105 SI181 0.2017937 % 4.48E-02 Figure 2 2008 Mar Mar Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 27.65000 27.6500 NA 3 3 28.48367 28.553 0.1157854 aut 9 fall 0.1098426 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4410629 301.89 0.000000
760 EI0836 CD105 SI181 0.6502242 % 0.38116592 Figure 2 2008 Apr Apr Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 26.38800 26.3880 NA 4 4 28.48367 28.553 0.1157854 aut 10 fall 0.6601987 NA NA 0.30000 1 Figure 2 NA 1 NA Quadrat 3 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4410629 301.89 0.000000
761 EI0837 CD105 SI181 0.4708520 % 0.24663677 Figure 2 2008 Aug Aug Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 22.66000 22.6600 NA 8 8 28.48367 28.553 0.1157854 win 2 winter 0.6041342 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9142761 301.89 0.000000
762 EI0838 CD105 SI181 2.0852018 % 0.71748879 Figure 2 2008 Nov Nov Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 27.29300 27.2930 NA 11 11 28.48367 28.553 0.1157854 spr 5 spring 1.7574814 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6057129 301.89 0.000000
763 EI0839 CD105 SI181 1.6816143 % 0.47085202 Figure 2 2008 Dec Dec Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.54800 28.5480 NA 12 12 28.48367 28.553 0.1157854 sum 6 summer 1.1533472 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6057129 301.89 0.000000
764 EI0840 CD105 SI181 1.5919283 % 0.53811659 Figure 2 2009 Jan Jan Figure 2 Pelorus Island, GBR Marine Park Materials and Methods Study site and field surveys Western Pacific Australia -18.550000 146.50000 -18.550000 146.50000 Materials and Methods Study site and field surveys, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Pacific Ocean 28.55300 28.5530 NA 1 1 28.42467 28.313 0.4285533 sum 7 summer 1.3181111 NA NA 0.60000 2 Figure 2 NA 1 NA Quadrat 6 10.0000 10.00 100.0000 Materials and Methods Study site and field surveys NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4207001 301.89 0.000000
765 EI0841 CD106 SI182 1.3000000 % 0.2090909 Figure 3 2011 Jan-Apr Jan-Apr Materials and Methods Study sites Grand Recif of Tulear, SW Madagascar Materials and Methods Study sites Western Indian Ocean West Indian -23.089610 43.60424 -23.089610 43.60424 GoogleMaps Grand Recif, Madagascar NA NA Prevalence extracted using MetaDigitise in Rstudio n = 30 Indian Ocean 28.75625 28.8000 0.4377280 1 4 28.15600 28.135 0.3789367 aut 7 summer 1.1452381 NA NA 3.00000 10 Materials and Methods 2.1 White syndrome prevalence NA 1 NA Belt 30 50.0000 2.00 100.0000 Materials and Methods White syndrome prevalence NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3635712 302.33 3.911400
766 EI0842 CD106 SI183 0.3818182 % 0.1636364 Figure 3 2011 Jan-Apr Jan-Apr Materials and Methods Study sites Adavadoaka Reef System, Madagascar Materials and Methods Study sites Western Indian Ocean West Indian -22.076950 43.23053 -22.076950 43.23053 Figure 1, GoogleMaps NA NA Prevalence extracted using MetaDigitise in Rstudio n = 30 Indian Ocean 29.15725 29.1415 0.3968820 1 4 28.52700 28.543 0.3642640 aut 7 summer 0.8962733 NA NA 3.00000 10 Materials and Methods 2.1 White syndrome prevalence NA 1 NA Belt 30 50.0000 2.00 100.0000 Materials and Methods White syndrome prevalence NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.9271469 302.59 1.037137
767 EI0843 CD107 SI184 4.3000000 % 1.8 Results Differential disease susceptibility among color morphs of M capitata 2007 May May Materials and Methods Susceptibility to coral disease Kaneohe Bay, Oahu, HI Materials and Methods Study site Western Pacific Polynesia 21.463460 -157.81028 21.463460 -157.81028 GoogleMaps Kaneohe Bay NA NA orange morph Pacific Ocean 25.00000 25.0000 NA 5 5 25.96267 25.940 0.3295856 spr 5 spring NA NA NA 2.70000 9 Materials and Methods Susceptibility to coral disease 3078 0 Coral_N includes both morphs Belt 18 25.0000 6.00 150.0000 Materials and Methods Susceptibility to coral disease NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6685486 300.02 0.000000
768 EI0844 CD107 SI184 0.6000000 % 0.4 Results Differential disease susceptibility among color morphs of M capitata 2007 May May Materials and Methods Susceptibility to coral disease Kaneohe Bay, Oahu, HI Materials and Methods Study site Western Pacific Polynesia 21.463460 -157.81028 21.463460 -157.81028 GoogleMaps Kaneohe Bay NA NA red morph Pacific Ocean 25.00000 25.0000 NA 5 5 25.96267 25.940 0.3295856 spr 5 spring NA NA NA 2.70000 9 Materials and Methods Susceptibility to coral disease 3078 0 Coral_N includes both morphs Belt 18 25.0000 6.00 150.0000 Materials and Methods Susceptibility to coral disease NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6685486 300.02 0.000000
769 EI0845 CD107 SI184 15.7000000 % 3.6 Results Differential disease susceptibility among color morphs of M capitata 2011 Jul Jul Materials and Methods Susceptibility to coral disease Kaneohe Bay, Oahu, HI Materials and Methods Study site Western Pacific Polynesia 21.463460 -157.81028 21.463460 -157.81028 GoogleMaps Kaneohe Bay NA NA orange morph Pacific Ocean 25.47000 25.4700 NA 7 7 25.54367 25.470 0.2993762 sum 7 summer NA NA NA 0.50000 10 Materials and Methods Susceptibility to coral disease 1370 0 Coral_N includes both morphs Belt 10 50.0000 1.00 50.0000 Materials and Methods Susceptibility to coral disease NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1885529 300.02 0.000000
770 EI0846 CD107 SI184 3.4000000 % 0.7 Results Differential disease susceptibility among color morphs of M capitata 2011 Jul Jul Materials and Methods Susceptibility to coral disease Kaneohe Bay, Oahu, HI Materials and Methods Study site Western Pacific Polynesia 21.463460 -157.81028 21.463460 -157.81028 GoogleMaps Kaneohe Bay NA NA red morph Pacific Ocean 25.47000 25.4700 NA 7 7 25.54367 25.470 0.2993762 sum 7 summer NA NA NA 0.50000 10 Materials and Methods Susceptibility to coral disease 1370 0 Coral_N includes both morphs Belt 10 50.0000 1.00 50.0000 Materials and Methods Susceptibility to coral disease NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1885529 300.02 0.000000
771 EI0847 CD108 SI185 3.6000000 % NA Table 1 2008 0 0 Table 1 Similan Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 10.961810 97.69758 10.961810 97.69758 GoogleMaps Similan Island NA NA NA Indian Ocean 28.61000 28.6100 NA 7 7 28.61033 28.610 0.3774997 sum 7 summer NA NA NA 1.20000 10 Table 1 1030 0 Coral_N aggregated over entire sampling year Belt 30 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2599792 302.78 0.000000
772 EI0848 CD108 SI185 2.1000000 % NA Table 1 2008 0 0 Table 1 Similan Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 10.961810 97.69758 10.961810 97.69758 GoogleMaps Similan Island NA NA NA Indian Ocean 28.61000 28.6100 NA 7 7 28.61033 28.610 0.3774997 sum 7 summer NA NA NA 1.20000 10 Table 1 1030 0 Coral_N aggregated over entire sampling year Belt 30 20.0000 2.00 40.0000 Material and Methods p57 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2599792 302.78 0.000000
773 EI0849 CD108 SI185 16.4000000 % NA Table 1 2008 0 0 Table 1 Similan Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 10.961810 97.69758 10.961810 97.69758 GoogleMaps Similan Island NA NA NA Indian Ocean 28.61000 28.6100 NA 7 7 28.61033 28.610 0.3774997 sum 7 summer NA NA NA 1.20000 10 Table 1 1030 0 Coral_N aggregated over entire sampling year Belt 30 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2599792 302.78 0.000000
774 EI0850 CD108 SI185 4.8000000 % NA Table 1 2010 Nov Nov Table 1 Similan Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 10.961810 97.69758 10.961810 97.69758 GoogleMaps Similan Island NA NA NA Indian Ocean 28.55300 28.5530 NA 11 11 29.22767 29.285 0.4318647 aut 11 fall NA NA NA 0.60000 5 Table 1 312 0 Coral_N aggregated over entire sampling year Belt 15 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5142822 302.78 11.725703
775 EI0851 CD108 SI185 0.6000000 % NA Table 1 2010 Nov Nov Table 1 Similan Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 10.961810 97.69758 10.961810 97.69758 GoogleMaps Similan Island NA NA NA Indian Ocean 28.55300 28.5530 NA 11 11 29.22767 29.285 0.4318647 aut 11 fall NA NA NA 0.60000 5 Table 1 312 0 Coral_N aggregated over entire sampling year Belt 15 20.0000 2.00 40.0000 Material and Methods p57 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5142822 302.78 11.725703
776 EI0852 CD108 SI185 8.0000000 % NA Table 1 2010 Nov Nov Table 1 Similan Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 10.961810 97.69758 10.961810 97.69758 GoogleMaps Similan Island NA NA NA Indian Ocean 28.55300 28.5530 NA 11 11 29.22767 29.285 0.4318647 aut 11 fall NA NA NA 0.60000 5 Table 1 312 0 Coral_N aggregated over entire sampling year Belt 15 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5142822 302.78 11.725703
777 EI0853 CD108 SI186 0.8000000 % NA Table 1 2009 0 0 Table 1 Surin Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 9.454450 97.87704 9.454450 97.87704 GoogleMaps Surin Islands NA NA NA Indian Ocean 29.04500 29.0450 NA 7 7 28.96600 29.045 0.1041298 sum 7 summer NA NA NA 0.84000 7 Table 1 247 0 Coral_N aggregated over entire sampling year Belt 21 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6314239 302.69 0.000000
778 EI0854 CD108 SI186 0.0000000 % NA Table 1 2009 0 0 Table 1 Surin Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 9.454450 97.87704 9.454450 97.87704 GoogleMaps Surin Islands NA NA NA Indian Ocean 29.04500 29.0450 NA 7 7 28.96600 29.045 0.1041298 sum 7 summer NA NA NA 0.84000 7 Table 1 247 0 Coral_N aggregated over entire sampling year Belt 21 20.0000 2.00 40.0000 Material and Methods p57 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6314239 302.69 0.000000
779 EI0855 CD108 SI186 8.5000000 % NA Table 1 2009 0 0 Table 1 Surin Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 9.454450 97.87704 9.454450 97.87704 GoogleMaps Surin Islands NA NA NA Indian Ocean 29.04500 29.0450 NA 7 7 28.96600 29.045 0.1041298 sum 7 summer NA NA NA 0.84000 7 Table 1 247 0 Coral_N aggregated over entire sampling year Belt 21 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6314239 302.69 0.000000
780 EI0856 CD108 SI186 22.3000000 % NA Table 1 2010 Nov Nov Table 1 Surin Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 9.454450 97.87704 9.454450 97.87704 GoogleMaps Surin Islands NA NA NA Indian Ocean 28.39300 28.3930 NA 11 11 29.21600 29.278 0.3689280 aut 11 fall NA NA NA 0.72000 6 Table 1 292 0 Coral_N aggregated over entire sampling year Belt 18 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4653473 302.69 11.891382
781 EI0857 CD108 SI186 0.3000000 % NA Table 1 2010 Nov Nov Table 1 Surin Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 9.454450 97.87704 9.454450 97.87704 GoogleMaps Surin Islands NA NA NA Indian Ocean 28.39300 28.3930 NA 11 11 29.21600 29.278 0.3689280 aut 11 fall NA NA NA 0.72000 6 Table 1 292 0 Coral_N aggregated over entire sampling year Belt 18 20.0000 2.00 40.0000 Material and Methods p57 NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4653473 302.69 11.891382
782 EI0858 CD108 SI186 9.2000000 % NA Table 1 2010 Nov Nov Table 1 Surin Island, Andaman Sea Material and Methods p57 Eastern Indian Ocean Southeast Asia 9.454450 97.87704 9.454450 97.87704 GoogleMaps Surin Islands NA NA NA Indian Ocean 28.39300 28.3930 NA 11 11 29.21600 29.278 0.3689280 aut 11 fall NA NA NA 0.72000 6 Table 1 292 0 Coral_N aggregated over entire sampling year Belt 18 20.0000 2.00 40.0000 Material and Methods p57 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4653473 302.69 11.891382
783 EI0859 CD109 SI187 12.2500000 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Vethalai, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
784 EI0860 CD109 SI187 17.9166670 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 T Nagar, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
785 EI0861 CD109 SI187 20.6250000 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Shooting thidal, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
786 EI0862 CD109 SI187 28.5416670 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Mandapam, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
787 EI0863 CD109 SI187 43.3333330 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Mandapam jetty, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
788 EI0864 CD109 SI187 15.2083330 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Koilvadi, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
789 EI0865 CD109 SI187 8.3333330 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Pampan, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
790 EI0866 CD109 SI187 7.7083330 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Thangachimadam, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
791 EI0867 CD109 SI187 9.7916670 % NA Figure 2 2009 Apr-Sep Apr-Sep Materials and Methods p813 Rameswaram north, Palk Bay Figure 2 Eastern Indian Ocean Central Indian 9.534950 79.24993 9.534950 79.24993 GoogleMaps Palk Bay NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Indian Ocean 29.09650 28.7540 0.6458702 4 9 28.69700 28.635 0.1546215 multi 4 spring NA NA NA 0.48000 2 Materials and Methods p814 4256 1 Coral_N aggregated from whole study Belt 6 20.0000 4.00 80.0000 Materials and Methods p814 Material and Methods p814 says “9 locations (18 sites…” so assumed 2 sites per location and 3 transects per site 6 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “white spot” classified as WPx 1.5328674 303.72 0.000000
792 EI0868 CD110 SI188 12.3076920 % 0.9615385 Figure 3 2016 Sep Sep Materials and Methods Sampling sites and environmental parameters p194 North Bali Island Figure 1 Coral Triangle & SE Asia Southeast Asia -8.140280 114.65485 -8.140280 114.65485 GoogleMaps Selini Beach Bali NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.10800 28.1080 NA 9 9 29.21767 29.210 0.2365939 spr 3 spring 1.6654335 NA NA 0.06000 1 Materials and Methods Sampling sites and environmental parameters p194 NA 1 massive Porites species Belt 3 10.0000 2.00 20.0000 Materials and Methods Sampling sites and environmental parameters p194 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 302.23 30.004151
793 EI0869 CD110 SI188 2.3076920 % 0.1923077 Figure 3 2016 Sep Sep Materials and Methods Sampling sites and environmental parameters p194 North Bali Island Figure 1 Coral Triangle & SE Asia Southeast Asia -8.140280 114.65485 -8.140280 114.65485 GoogleMaps Selini Beach Bali NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.10800 28.1080 NA 9 9 29.21767 29.210 0.2365939 spr 3 spring 0.3330867 NA NA 0.06000 1 Materials and Methods Sampling sites and environmental parameters p194 NA 1 massive Porites species Belt 3 10.0000 2.00 20.0000 Materials and Methods Sampling sites and environmental parameters p194 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 302.23 30.004151
794 EI0870 CD110 SI188 46.9230770 % 1.7307692 Figure 3 2016 Sep Sep Materials and Methods Sampling sites and environmental parameters p194 North Bali Island Figure 1 Coral Triangle & SE Asia Southeast Asia -8.140280 114.65485 -8.140280 114.65485 GoogleMaps Selini Beach Bali NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.10800 28.1080 NA 9 9 29.21767 29.210 0.2365939 spr 3 spring 2.9977802 NA NA 0.06000 1 Materials and Methods Sampling sites and environmental parameters p194 NA 1 massive Porites species Belt 3 10.0000 2.00 20.0000 Materials and Methods Sampling sites and environmental parameters p194 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 302.23 30.004151
795 EI0871 CD110 SI188 2.3076920 % 0.1923077 Figure 3 2016 Sep Sep Materials and Methods Sampling sites and environmental parameters p194 North Bali Island Figure 1 Coral Triangle & SE Asia Southeast Asia -8.140280 114.65485 -8.140280 114.65485 GoogleMaps Selini Beach Bali NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.10800 28.1080 NA 9 9 29.21767 29.210 0.2365939 spr 3 spring 0.3330867 NA NA 0.06000 1 Materials and Methods Sampling sites and environmental parameters p194 NA 1 massive Porites species Belt 3 10.0000 2.00 20.0000 Materials and Methods Sampling sites and environmental parameters p194 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 302.23 30.004151
796 EI0872 CD110 SI188 76.9230770 % 0.9615385 Figure 3 2016 Sep Sep Materials and Methods Sampling sites and environmental parameters p194 North Bali Island Figure 1 Coral Triangle & SE Asia Southeast Asia -8.140280 114.65485 -8.140280 114.65485 GoogleMaps Selini Beach Bali NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.10800 28.1080 NA 9 9 29.21767 29.210 0.2365939 spr 3 spring 1.6654335 NA NA 0.06000 1 Materials and Methods Sampling sites and environmental parameters p194 NA 1 massive Porites species Belt 3 10.0000 2.00 20.0000 Materials and Methods Sampling sites and environmental parameters p194 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 302.23 30.004151
797 EI0873 CD110 SI188 90.5769230 % 0.7692308 Figure 3 2016 Sep Sep Materials and Methods Sampling sites and environmental parameters p194 North Bali Island Figure 1 Coral Triangle & SE Asia Southeast Asia -8.140280 114.65485 -8.140280 114.65485 GoogleMaps Selini Beach Bali NA NA Prevalence extracted using MetaDigitise in Rstudio n = 3 Pacific Ocean 28.10800 28.1080 NA 9 9 29.21767 29.210 0.2365939 spr 3 spring 1.3323468 NA NA 0.06000 1 Materials and Methods Sampling sites and environmental parameters p194 NA 1 massive Porites species Belt 3 10.0000 2.00 20.0000 Materials and Methods Sampling sites and environmental parameters p194 NA 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5814209 302.23 30.004151
798 EI0874 CD111 SI189 0.6875000 % 0.25 Figure 3 2005 0 0 Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 28.98800 28.9880 NA 7 7 28.98433 28.988 0.3525143 sum 7 summer 2.5980760 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4714050 301.54 1.108560
799 EI0875 CD111 SI189 9.8125000 % 1.75 Figure 3 2006 Jan-May Jan-May Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 26.72720 26.1850 0.6833574 1 5 28.51000 28.395 0.2167377 multi 1 winter 18.1865330 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2760696 301.54 12.398520
800 EI0876 CD111 SI189 4.1875000 % 0.8125 Figure 3 2006 Jun-Dec Jun-Dec Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 28.60829 29.4150 0.6229745 6 12 28.51000 28.395 0.2167377 multi 6 summer 8.4437480 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6828613 301.54 12.398520
801 EI0877 CD111 SI189 0.2500000 % 0.125 Figure 3 2007 0 0 Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 28.81000 28.8100 NA 7 7 28.72200 28.810 0.2093630 sum 7 summer 1.2990380 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2164307 301.54 3.508573
802 EI0878 CD111 SI189 0.4375000 % 0.25 Figure 3 2008 0 0 Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 28.45300 28.4530 NA 7 7 28.49633 28.453 0.5063926 sum 7 summer 2.5980760 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5399780 301.54 0.000000
803 EI0879 CD111 SI189 0.5625000 % 0.3125 Figure 3 2009 0 0 Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 28.52000 28.5200 NA 7 7 28.37167 28.520 0.4225026 sum 7 summer 3.2475950 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2467728 301.54 0.000000
804 EI0880 CD111 SI189 0.3125000 % 0.125 Figure 3 2010 0 0 Figure 3 US Virgin Islands Methods Caribbean & Gulf of Mexico Caribbean/Atlantic 18.920320 -64.95859 18.920320 -64.95859 GoogleMaps US Virgin Islands NA NA Prevalence extracted using MetaDigitise in Rstudio n = 108 Atlantic Ocean 29.10500 29.1050 NA 7 7 29.16267 29.105 0.4016176 sum 7 summer 1.2990380 NA NA 1.08000 18 Methods 9989 1 coral_n aggregated over whole study Line 108 10.0000 1.00 10.0000 Methods NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.0799866 301.54 0.000000
805 EI0881 CD112 SI190 0.0000000 % 9.76E-02 Figure 6 1994 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 22 Atlantic Ocean 29.25500 29.2550 NA 7 7 29.13600 29.255 0.4116106 sum 7 summer 0.4578046 NA NA 4.40000 22 Figure 6 NA NA NA Belt 22 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8582153 302.71 0.000000
806 EI0882 CD112 SI190 0.8695652 % 0.14196983 Figure 6 1995 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 15 Atlantic Ocean 29.64500 29.6450 NA 7 7 29.35600 29.645 0.5694499 sum 7 summer 0.5498468 NA NA 3.00000 15 Figure 6 NA NA NA Belt 15 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4735794 302.71 0.000000
807 EI0883 CD112 SI190 0.9130435 % 0.17524401 Figure 6 1996 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 11 Atlantic Ocean 29.62300 29.6230 NA 7 7 29.35300 29.623 0.5565070 sum 7 summer 0.5812186 NA NA 2.20000 11 Figure 6 NA NA NA Belt 11 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8971558 302.71 0.000000
808 EI0884 CD112 SI190 0.0000000 % 0.24401065 Figure 6 1997 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 6 Atlantic Ocean 30.21300 30.2130 NA 7 7 29.89767 30.213 0.9980865 sum 7 summer 0.5977016 NA NA 1.20000 6 Figure 6 NA NA NA Belt 6 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3157501 302.71 0.000000
809 EI0885 CD112 SI190 0.5826087 % 0.12644188 Figure 6 1998 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 12 Atlantic Ocean 30.12800 30.1280 NA 7 7 29.92967 30.128 0.7329106 sum 7 summer 0.4380075 NA NA 2.40000 12 Figure 6 NA NA NA Belt 12 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1171570 302.71 4.505706
810 EI0886 CD112 SI190 0.1130435 % 0.19299024 Figure 6 2001 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 9 Atlantic Ocean 29.31500 29.3150 NA 7 7 29.18267 29.315 0.6664279 sum 7 summer 0.5789707 NA NA 1.80000 9 Figure 6 NA NA NA Belt 9 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6596680 302.71 0.000000
811 EI0887 CD112 SI190 0.4217391 % 0.12200532 Figure 6 2002 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 19 Atlantic Ocean 29.57000 29.5700 NA 7 7 29.28100 29.570 0.8463531 sum 7 summer 0.5318089 NA NA 3.80000 19 Figure 6 NA NA NA Belt 19 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9699860 302.71 0.000000
812 EI0888 CD112 SI190 0.2000000 % 0.14862467 Figure 6 2004 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 15 Atlantic Ocean 30.03000 30.0300 NA 7 7 29.70267 30.030 0.6917527 sum 7 summer 0.5756209 NA NA 3.00000 15 Figure 6 NA NA NA Belt 15 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4207153 302.71 0.000000
813 EI0889 CD112 SI190 0.0000000 % 9.76E-02 Figure 6 2005 0 0 Figure 6 Florida Keys National Marine Sanctuary Materials and Methods White plague type ii data collection Caribbean & Gulf of Mexico Caribbean/Atlantic 24.574000 -83.13982 24.574000 -83.13982 GoogleMaps Florida Keys National Marine Sanctuary NA NA Prevalence extracted using MetaDigitise in Rstudio n = 22 Atlantic Ocean 29.61300 29.6130 NA 7 7 29.48634 29.613 0.9712149 sum 7 summer 0.4578046 NA NA 4.40000 22 Figure 6 NA NA NA Belt 22 200.0000 1.00 200.0000 Materials and Methods White plague type ii data collection NA 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5742798 302.71 0.000000
814 EI0890 CD113 SI191 30.4000000 % NA Results Disease prevalence and incidence rate p9 1994 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.37800 29.3780 NA 7 7 29.27267 29.378 0.4151469 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 92 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.9589157 302.79 1.090005
815 EI0891 CD113 SI191 50.7000000 % NA Results Disease prevalence and incidence rate p9 1995 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.71300 29.7130 NA 7 7 29.40867 29.713 0.5594703 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 69 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7863998 302.79 0.000000
816 EI0892 CD113 SI191 57.7000000 % NA Results Disease prevalence and incidence rate p9 1996 Oct Oct Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 28.00500 28.0050 NA 10 10 29.30533 29.528 0.5508550 aut 10 fall NA NA NA 13.50000 1 Material and Methods p7 52 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7700195 302.79 0.000000
817 EI0893 CD113 SI191 60.9000000 % NA Results Disease prevalence and incidence rate p9 1997 Jun Jun Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 28.83500 28.8350 NA 6 6 29.94600 30.268 0.9900823 sum 6 summer NA NA NA 13.50000 1 Material and Methods p7 46 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3528290 302.79 0.000000
818 EI0894 CD113 SI191 64.3000000 % NA Results Disease prevalence and incidence rate p9 1997 Sep Sep Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.73800 29.7380 NA 9 9 29.94600 30.268 0.9900823 aut 9 fall NA NA NA 13.50000 1 Material and Methods p7 42 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4221497 302.79 8.314264
819 EI0895 CD113 SI191 62.1000000 % NA Results Disease prevalence and incidence rate p9 1998 May May Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 26.72300 26.7230 NA 5 5 30.05700 30.303 0.7259636 spr 5 spring NA NA NA 13.50000 1 Material and Methods p7 29 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.0099945 302.79 8.314264
820 EI0896 CD113 SI191 71.4000000 % NA Results Disease prevalence and incidence rate p9 1998 Sep Sep Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.59300 29.5930 NA 9 9 30.05700 30.303 0.7259636 aut 9 fall NA NA NA 13.50000 1 Material and Methods p7 21 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4550018 302.79 6.225684
821 EI0897 CD113 SI191 44.4000000 % NA Results Disease prevalence and incidence rate p9 1999 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.45300 29.4530 NA 7 7 29.45433 29.453 0.8800009 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 9 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4550018 302.79 6.225684
822 EI0898 CD113 SI191 33.3000000 % NA Results Disease prevalence and incidence rate p9 2000 Dec Dec Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 24.94300 24.9430 NA 12 12 29.36533 29.813 0.8589678 win 12 winter NA NA NA 13.50000 1 Material and Methods p7 3 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1993027 302.79 0.000000
823 EI0899 CD113 SI191 0.0000000 % NA Results Disease prevalence and incidence rate p9 2001 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.35500 29.3550 NA 7 7 29.25034 29.355 0.7008858 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 3 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6314239 302.79 0.000000
824 EI0900 CD113 SI191 50.0000000 % NA Results Disease prevalence and incidence rate p9 2002 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.60800 29.6080 NA 7 7 29.33134 29.608 0.8927529 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 2 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.7953568 302.79 1.409982
825 EI0901 CD113 SI191 0.0000000 % NA Results Disease prevalence and incidence rate p9 2003 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 29.54000 29.5400 NA 7 7 29.46200 29.540 0.6037899 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 1 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.3203430 302.79 0.000000
826 EI0902 CD113 SI191 50.0000000 % NA Results Disease prevalence and incidence rate p9 2004 Jul Jul Material and Methods p7 Eastern Dry Rocks Reef Florida Keys Marine Sanctuary Material and Methods p7 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.460283 -81.84305 24.460283 -81.84305 Material and Methods p7, GoogleMaps NA NA NA Atlantic Ocean 30.08000 30.0800 NA 7 7 29.74267 30.080 0.7135448 sum 7 summer NA NA NA 13.50000 1 Material and Methods p7 2 0 NA Quadrat 36 0.7500 0.25 0.1875 Material and Methods p7 Quadrats contiguous to create one large sample area; for some reason this math doesn’t add up… 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3414307 302.79 0.000000
827 EI0903 CD114 SI192 21.7000000 % NA Results 2015 Oct-Nov Oct-Nov Methods Disease prevalence surveys, lesion progression monitoring and sampling Luminao Reef, Guam Results Western Pacific Micronesia 16.675260 144.08563 16.675260 144.08563 GoogleMaps Guam NA NA NA Pacific Ocean 28.95300 28.9530 0.2828424 10 11 29.29867 29.353 0.1634204 aut 10 fall NA NA NA 0.06000 1 Methods Disease prevalence surveys, lesion progression monitoring and sampling 53 1 NA Belt 3 20.0000 1.00 20.0000 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 NA 0.5971375 302.41 0.000000
828 EI0904 CD114 SI193 0.0000000 % NA Results 2011 Jun Jun Methods Disease prevalence surveys, lesion progression monitoring and sampling Philippines Results Coral Triangle & SE Asia Southeast Asia 11.208060 123.73960 11.208060 123.73960 GoogleMaps Bantayan Island NA NA NA Pacific Ocean 29.51000 29.5100 NA 6 6 29.25433 29.240 0.2488106 sum 6 summer NA NA NA 1.08000 18 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 NA Belt 54 20.0000 1.00 20.0000 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 NA 0.6021194 302.69 6.459946
829 EI0905 CD114 SI193 0.0000000 % NA Results 2015 Apr Apr Methods Disease prevalence surveys, lesion progression monitoring and sampling Philippines Results Coral Triangle & SE Asia Southeast Asia 11.208060 123.73960 11.208060 123.73960 GoogleMaps Bantayan Island NA NA NA Pacific Ocean 28.20000 28.2000 NA 4 4 29.76200 29.345 0.4494139 spr 4 spring NA NA NA 1.08000 18 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 NA Belt 54 20.0000 1.00 20.0000 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 NA 0.3374863 302.69 0.000000
830 EI0906 CD114 SI194 0.0000000 % NA Results 2018 May May Methods Disease prevalence surveys, lesion progression monitoring and sampling Palau Results Western Pacific Micronesia 8.401070 134.57791 8.401070 134.57791 GoogleMaps Palau NA NA NA Pacific Ocean 30.04000 30.0400 NA 5 5 29.43533 29.508 0.4504181 spr 5 spring NA NA NA 0.06000 1 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 NA Belt 3 20.0000 1.00 20.0000 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 NA 0.8250275 302.41 8.115692
831 EI0907 CD114 SI195 0.0000000 % NA Results 2017 Sep Sep Methods Disease prevalence surveys, lesion progression monitoring and sampling Maldives Results Western Indian Ocean Central Indian 7.999790 72.69305 7.999790 72.69305 GoogleMaps Maldives NA NA NA Indian Ocean 28.28800 28.2880 NA 9 9 28.83767 28.790 0.2716551 aut 9 fall NA NA NA 0.06000 1 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 NA Belt 3 20.0000 1.00 20.0000 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 NA 1.7156982 303.15 0.000000
832 EI0908 CD114 SI196 0.0000000 % NA Results 2018 Mar Mar Methods Disease prevalence surveys, lesion progression monitoring and sampling Reunion Results Western Indian Ocean West Indian -19.071860 55.17569 -19.071860 55.17569 GoogleMaps Reunion Island NA NA NA Indian Ocean 28.12000 28.1200 NA 3 3 28.31100 28.480 0.8364045 aut 9 fall NA NA NA 0.06000 1 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 NA Belt 3 20.0000 1.00 20.0000 Methods Disease prevalence surveys, lesion progression monitoring and sampling NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 NA 0.2735672 301.12 5.499953
833 EI0909 CD115 SI197 8.0000000 % NA Results p71 2009 Jan Jan Results p71 Shingle Island Materials and Methods p70 Eastern Indian Ocean Central Indian 9.241760 79.23592 9.241760 79.23592 GoogleMaps Shingle Island NA NA NA Indian Ocean 27.18500 27.1850 NA 1 1 28.69700 28.635 0.1546215 win 1 winter NA NA NA 0.24000 1 Figure 1 2910 1 Coral_N aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods p70 NA 4 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7635880 303.56 0.000000
834 EI0910 CD115 SI197 26.9000000 % NA Results p71 2009 Dec Dec Results p71 Shingle Island Materials and Methods p70 Eastern Indian Ocean Central Indian 9.241760 79.23592 9.241760 79.23592 GoogleMaps Shingle Island NA NA NA Indian Ocean 28.36300 28.3630 NA 12 12 28.69700 28.635 0.1546215 win 12 winter NA NA NA 0.24000 1 Figure 1 2910 1 Coral_N aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods p70 NA 4 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.7785645 303.56 0.000000
835 EI0911 CD115 SI197 41.9000000 % NA Results p71 2010 Dec Dec Results p71 Shingle Island Materials and Methods p70 Eastern Indian Ocean Central Indian 9.241760 79.23592 9.241760 79.23592 GoogleMaps Shingle Island NA NA NA Indian Ocean 27.59500 27.5950 NA 12 12 28.84533 28.833 0.4036409 win 12 winter NA NA NA 0.24000 1 Figure 1 2910 1 Coral_N aggregated from whole study Belt 3 20.0000 4.00 80.0000 Materials and Methods p70 NA 4 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4371338 303.56 4.147088
836 EI0912 CD116 SI198 0.6600000 % NA Table 1 2002 Jun-Jul Jun-Jul Materials and Methods Surveys p34 Lee Stocking Island, Bahamas Materials and Methods Surveys p34 Caribbean & Gulf of Mexico Caribbean/Atlantic 23.773530 -76.09886 23.773530 -76.09886 GoogleMaps Lee Stocking Island NA NA NA Atlantic Ocean 28.28050 28.2805 0.7106417 6 7 28.65133 28.783 0.8155109 sum 6 summer NA NA NA 9.42000 10 Materials and Methods Surveys p34 11092 1 NA Circle 150 20.0000 NA 314.0000 Materials and Methods Surveys p34 length is diameter 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3335495 302.24 0.000000
837 EI0913 CD116 SI198 0.6100000 % NA Table 1 2003 Jun-Jul Jun-Jul Materials and Methods Surveys p34 Lee Stocking Island, Bahamas Materials and Methods Surveys p34 Caribbean & Gulf of Mexico Caribbean/Atlantic 23.773530 -76.09886 23.773530 -76.09886 GoogleMaps Lee Stocking Island NA NA NA Atlantic Ocean 28.38800 28.3880 0.3181972 6 7 28.58867 28.613 0.4140364 sum 6 summer NA NA NA 10.36200 10 Materials and Methods Surveys p34 13973 1 NA Circle 165 20.0000 NA 314.0000 Materials and Methods Surveys p34 length is diameter 3 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.1700134 302.24 1.052847
838 EI0914 CD117 SI199 0.3000000 % NA Table 2 2008 Jul-Aug, Oct-Nov Jul-Aug, Oct-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA NA Pacific Ocean 28.17280 28.3730 0.1796198 7 11 27.96367 28.043 0.2795733 multi 7 summer NA NA NA 11.80000 12 Table 1 51444 1 NA Belt 59 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “Discoloration necrosis” classified as Pigmentation Response 0.3535919 302.10 0.000000
839 EI0915 CD117 SI199 0.2700000 % NA Table 3 2009 Jul, Oct-Nov Jul-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA Simplified month sampling period for data analysis Pacific Ocean 29.41940 29.3100 0.2592918 7 11 29.07634 29.133 0.2547710 multi 7 summer NA NA NA 2.00000 12 Table 1 NA 1 NA Belt 40 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414612 302.10 0.000000
840 EI0916 CD117 SI199 3.4400000 % NA Table 3 2009 Jul, Oct-Nov Jul-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA Simplified month sampling period for data analysis Pacific Ocean 29.41940 29.3100 0.2592918 7 11 29.07634 29.133 0.2547710 multi 7 summer NA NA NA 2.00000 12 Table 1 NA 1 NA Belt 40 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414612 302.10 0.000000
841 EI0917 CD117 SI199 3.0100000 % NA Table 3 2009 Jul, Oct-Nov Jul-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA Simplified month sampling period for data analysis Pacific Ocean 29.41940 29.3100 0.2592918 7 11 29.07634 29.133 0.2547710 multi 7 summer NA NA NA 2.00000 12 Table 1 NA 1 NA Belt 40 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414612 302.10 0.000000
842 EI0918 CD117 SI199 0.5100000 % NA Table 3 2009 Jul, Oct-Nov Jul-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA Simplified month sampling period for data analysis Pacific Ocean 29.41940 29.3100 0.2592918 7 11 29.07634 29.133 0.2547710 multi 7 summer NA NA NA 2.00000 12 Table 1 NA 1 NA Belt 40 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414612 302.10 0.000000
843 EI0919 CD117 SI199 1.1800000 % NA Table 3 2009 Jul, Oct-Nov Jul-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA Simplified month sampling period for data analysis Pacific Ocean 29.41940 29.3100 0.2592918 7 11 29.07634 29.133 0.2547710 multi 7 summer NA NA NA 2.00000 12 Table 1 NA 1 NA Belt 40 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8414612 302.10 0.000000
844 EI0920 CD117 SI199 0.0800000 % NA Table 3 2009 Jul, Oct-Nov Jul-Nov Materials and Methods Disease spatial and temporal patterns p90 Palmyra Atoll Table 2 Western Pacific Micronesia 5.866667 -162.10000 5.866667 -162.10000 Materials and Methods Study site p90, GoogleMaps NA NA Simplified month sampling period for data analysis Pacific Ocean 29.41940 29.3100 0.2592918 7 11 29.07634 29.133 0.2547710 multi 7 summer NA NA NA 2.00000 12 Table 1 NA 1 NA Belt 40 50.0000 4.00 200.0000 Materials and Methods Disease spatial and temporal patterns p91 Transect diameter changes halfway down transect, took average of widths 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 “Discoloration necrosis” classified as Pigmentation Response 0.8414612 302.10 0.000000
845 EI0921 CD118 SI200 59.0000000 % 7.6 Results Coral lesions in response to biotic factors Pigmentation response p80 2015 May May Materials and Methods Survey technique p79 Kish Island Figure 1 Western Indian Ocean Middle East 26.533333 53.96667 26.533333 53.96667 Materials and Methods Study area p78 NA NA NA Indian Ocean 28.23500 28.2350 NA 5 5 31.87133 31.828 1.5354585 spr 5 spring NA NA NA 0.48000 4 Material and Methods Survey technique p78 474 1 NA Belt 12 20.0000 2.00 40.0000 Materials and Methods Survey technique p79 Transect width averaged - real width between 1-3m 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3114014 306.40 0.000000
846 EI0922 CD118 SI200 40.0000000 % 4.8 Results Coral lesions in response to biotic factors Pigmentation response p80 2015 May May Materials and Methods Survey technique p79 Kish Island Figure 1 Western Indian Ocean Middle East 26.533333 53.96667 26.533333 53.96667 Materials and Methods Study area p78 NA NA NA Indian Ocean 28.23500 28.2350 NA 5 5 31.87133 31.828 1.5354585 spr 5 spring NA NA NA 0.48000 4 Material and Methods Survey technique p78 474 1 NA Belt 12 20.0000 2.00 40.0000 Materials and Methods Survey technique p79 Transect width averaged - real width between 1-3m 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3114014 306.40 0.000000
847 EI0923 CD118 SI200 8.0000000 % 2.3 Result Coral lesions in response to biotic factors Disease p80 2015 May May Materials and Methods Survey technique p79 Kish Island Figure 1 Western Indian Ocean Middle East 26.533333 53.96667 26.533333 53.96667 Materials and Methods Study area p78 NA NA NA Indian Ocean 28.23500 28.2350 NA 5 5 31.87133 31.828 1.5354585 spr 5 spring NA NA NA 0.48000 4 Material and Methods Survey technique p78 474 1 NA Belt 12 20.0000 2.00 40.0000 Materials and Methods Survey technique p79 Transect width averaged - real width between 1-3m 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3114014 306.40 0.000000
848 EI0924 CD119 SI201 1.2000000 % NA Results p77 2004 May May Materials and Methods p76 shallow barrier reef Dos Masquises Sur, National Park of Los Roques, Venezuela Materials and Methods p76 Caribbean & Gulf of Mexico Caribbean/Atlantic 13.425100 -66.89277 13.425100 -66.89277 GoogleMaps Dos Mosquises Sur NA NA NA Atlantic Ocean 27.52800 27.5280 NA 5 5 28.01667 27.880 0.4075644 spr 5 spring NA NA NA 0.25500 2 Materials and Methods p76 1266 1 NA Belt-Quadrat 5 25.0000 2.00 55.0000 Materials and Methods p76 Sampling done in each site differently, data presented here for A palmata-dominated site sampling methods, plot area added 5m^2 for the area of the A cervicornis-dominated site sample area 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 NA 0.7953567 301.46 7.689955
849 EI0925 CD119 SI201 2.6000000 % NA Results p77 2004 May May Materials and Methods p76 shallow fringing reef Dos Masquises Sur, National Park of Los Roques, Venezuela Materials and Methods p76 Caribbean & Gulf of Mexico Caribbean/Atlantic 13.425100 -66.89277 13.425100 -66.89277 GoogleMaps Dos Mosquises Sur NA NA NA Atlantic Ocean 27.52800 27.5280 NA 5 5 28.01667 27.880 0.4075644 spr 5 spring NA NA NA 0.15000 1 Materials and Methods p76 513 1 NA Belt 3 25.0000 2.00 50.0000 Materials and Methods p76 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 NA 0.7953567 301.46 7.689955
850 EI0926 CD119 SI201 9.0000000 % NA Results p78 2004 May May Materials and Methods p76 deep fringing reef Dos Masquises Sur, National Park of Los Roques, Venezuela Materials and Methods p76 Caribbean & Gulf of Mexico Caribbean/Atlantic 13.425100 -66.89277 13.425100 -66.89277 GoogleMaps Dos Mosquises Sur NA NA NA Atlantic Ocean 27.52800 27.5280 NA 5 5 28.01667 27.880 0.4075644 spr 5 spring NA NA NA 0.07500 1 Materials and Methods p76 91 1 NA Line 3 25.0000 1.00 25.0000 Materials and Methods p76 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 NA 0.7953567 301.46 7.689955
851 EI0927 CD120 SI202 1.6000000 % NA Results and Disucssions p188 2005 Mar Mar Results and Disucssions p188 Bocas del Toro, Panama Results and Disucssions p188 Caribbean & Gulf of Mexico Caribbean/Atlantic 9.543820 -82.29074 9.543820 -82.29074 GoogleMaps Bocas del Toro NA NA NA Atlantic Ocean 28.08000 28.0800 NA 3 3 28.94333 28.800 0.2526026 spr 3 spring NA NA NA 1.80000 6 Results and Disucssions p188 23869 1 NA Belt 90 10.0000 2.00 20.0000 Results and Disucssions p188 NA 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 NA 0.5253372 301.58 4.837107
852 EI0930 CD122 SI203 2.0000000 % NA Results p27 2003 Aug, Dec Aug, Dec Materials and Methods p124 St John, US Virgin Islands Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.345960 -64.72921 18.335070 -64.69532 GoogleMaps St John USVI NA NA Prevalence extracted using MetaDigitise in Rstudio n = 23 Atlantic Ocean 28.48880 29.2580 0.7108951 8 12 27.95200 27.880 0.4889915 multi 8 summer NA NA NA 760.32400 29 Figure 1 8473 0 NA Belt 29 NA NA 26218.0700 Materials and Methods p125 entire sites surveyed, transects only used to divide area into more manageable sections; input here average site size 2 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Only most common diseases mentioned (Tissue necrosis and White pox) 0.3571396 301.55 0.000000
853 EI0931 CD123 SI204 23.5000000 % 11.7 Discussion Health status of Kure coral communities p18 2002 Sep Sep Materials and Methods Benthic surveys p3 Kure Atoll Discussion Health status of Kure coral communities p18 Western Pacific Polynesia 28.399940 -178.29324 28.399940 -178.29324 GoogleMaps Kure Atoll NA NA NA Pacific Ocean 27.48000 27.4800 NA 9 9 26.90200 27.308 1.5202197 aut 9 fall NA NA NA 0.70000 14 Results Site-specific surveys: video transects p7 NA 1 NA Belt 28 25.0000 1.00 25.0000 Materials and Methods Benthic surveys p3 (Maragos et al. 2004) NA 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3078613 299.93 7.092862
854 EI0932 CD123 SI205 7.9000000 % 3.1 Discussion Health status of Kure coral communities p18 2002 Sep Sep Materials and Methods Benthic surveys p3 NWHI (except Kure Atoll) Discussion Health status of Kure coral communities p18 Western Pacific Polynesia 26.142690 -170.49279 26.142690 -170.49279 GoogleMaps Kure Atoll NA NA NA Pacific Ocean 27.31500 27.3150 NA 9 9 26.63033 26.538 1.0016966 aut 9 fall NA NA NA 0.70000 14 Results Site-specific surveys: video transects p7 NA 1 NA Belt 28 25.0000 1.00 25.0000 Materials and Methods Benthic surveys p3 (Maragos et al. 2004) NA 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8082047 300.08 1.022845
855 EI0933 CD021 SI206 1.7567570 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 27.00000 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
856 EI0934 CD021 SI206 1.9729730 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 27.48649 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
857 EI0935 CD021 SI206 2.0540540 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 27.81081 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
858 EI0936 CD021 SI206 2.0810810 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 28.22973 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
859 EI0937 CD021 SI206 3.0810810 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 28.47297 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
860 EI0938 CD021 SI206 4.0000000 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 28.68919 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
861 EI0939 CD021 SI206 3.4054050 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 28.77027 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
862 EI0940 CD021 SI206 4.8108110 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 28.98649 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
863 EI0941 CD021 SI206 5.0540540 % NA Figure 1 2009 May-Sep May-Sep Methods Prevalence and incidence of BBD and water temperature p53 Tague Bay, St. Croix, US Virgin Islands Methods Prevalence and incidence of BBD and water temperature p53 Caribbean & Gulf of Mexico Caribbean/Atlantic 17.764167 -64.61944 17.764167 -64.61944 Methods Prevalence and incidence of BBD and water temperature p53, GoogleMaps 29.12162 Figure 1 Prevalence and temperature data extracted using MetaDigitise in Rstudio, n = 1, n = 1 Atlantic Ocean 28.32840 28.5530 0.8002126 5 9 28.47033 28.553 0.3729362 multi 5 spring NA NA NA 12.83300 1 Methods Prevalence and incidence of BBD and water temperature p53 966 0 NA Belt 1 313.0000 41.00 12833.0000 Methods Prevalence and incidence of BBD and water temperature p53 No transects, one large sample area 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1192780 301.66 0.000000
864 EI0942 CD023 SI207 1.1000000 % NA Results p69 1999 0 0 Results p69 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA NA Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 1.00000 50 Materials and Methods p68 2166 1 NA Belt 100 10.0000 1.00 10.0000 AGRRA protocol p14 https://www.researchgate.net/publication/265148106_Agrra_protocols_version_54 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
865 EI0943 CD023 SI207 0.1000000 % NA Results p69 1999 0 0 Results p69 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA NA Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 1.00000 50 Materials and Methods p68 NA 1 NA Belt 100 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
866 EI0944 CD023 SI207 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
867 EI0945 CD023 SI207 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
868 EI0946 CD023 SI207 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
869 EI0947 CD023 SI207 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
870 EI0948 CD023 SI207 0.0000000 % NA Figure 3 1995 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 28.68300 28.6830 NA 7 7 28.47533 28.683 0.3903919 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.2285461 301.79 0.000000
871 EI0949 CD023 SI207 2.0512820 % NA Figure 3 1996 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
872 EI0950 CD023 SI207 2.8205130 % NA Figure 3 1996 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
873 EI0951 CD023 SI207 0.0000000 % NA Figure 3 1996 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
874 EI0952 CD023 SI207 0.0000000 % NA Figure 3 1996 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
875 EI0953 CD023 SI207 0.0000000 % NA Figure 3 1996 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 50 Atlantic Ocean 27.96300 27.9630 NA 7 7 27.95534 27.963 0.2186010 sum 7 summer NA NA NA 15.70000 50 Materials and Methods p68 NA 0 Montastraea annularis sp complex Circle 50 10.0000 NA 314.0000 Materials and Methods p68 length is radius 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.5310898 301.79 1.128580
876 EI0954 CD023 SI207 15.1282050 % NA Figure 3 1999 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
877 EI0955 CD023 SI207 52.0000000 % NA Results p69 1999 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA NA Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
878 EI0956 CD023 SI207 19.1025640 % NA Figure 3 1999 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
879 EI0957 CD023 SI207 6.2820510 % NA Figure 3 1999 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
880 EI0958 CD023 SI207 1.5384620 % NA Figure 3 1999 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.37300 28.3730 NA 7 7 28.62033 28.373 0.3984679 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.1535645 301.79 1.011423
881 EI0959 CD023 SI207 26.1538460 % NA Figure 3 2000 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
882 EI0960 CD023 SI207 58.2051280 % NA Figure 3 2000 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
883 EI0961 CD023 SI207 18.2051280 % NA Figure 3 2000 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
884 EI0962 CD023 SI207 9.3589740 % NA Figure 3 2000 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
885 EI0963 CD023 SI207 2.3076920 % NA Figure 3 2000 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.04000 28.0400 NA 7 7 28.02333 28.040 0.4452337 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3117905 301.79 2.081409
886 EI0964 CD023 SI207 24.7435900 % NA Figure 3 2001 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
887 EI0965 CD023 SI207 54.4871790 % NA Figure 3 2001 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
888 EI0966 CD023 SI207 18.5897440 % NA Figure 3 2001 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
889 EI0967 CD023 SI207 11.1538460 % NA Figure 3 2001 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
890 EI0968 CD023 SI207 3.7179490 % NA Figure 3 2001 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.31000 28.3100 NA 7 7 28.35267 28.310 0.3509506 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.3192978 301.79 0.000000
891 EI0969 CD023 SI207 15.5128210 % NA Figure 3 2003 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
892 EI0970 CD023 SI207 39.2307690 % NA Figure 3 2003 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
893 EI0971 CD023 SI207 14.2307690 % NA Figure 3 2003 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
894 EI0972 CD023 SI207 16.0256410 % NA Figure 3 2003 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
895 EI0973 CD023 SI207 3.7179490 % NA Figure 3 2003 0 0 Figure 3 Mona Island Figure 1 Caribbean & Gulf of Mexico Caribbean/Atlantic 18.095390 -67.89445 18.095390 -67.89445 GoogleMaps Isla de Mona NA NA Prevalence extracted using MetaDigitise in Rstudio n = 20 Atlantic Ocean 28.07300 28.0730 NA 7 7 28.13533 28.073 0.3625419 sum 7 summer NA NA NA 0.20000 10 Materials and Methods p68 NA 0 Montastraea annularis sp complex Belt 20 10.0000 1.00 10.0000 AGRRA protocol p14 NA 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4449768 301.79 0.000000
896 EI0974 CD005 SI208 2.9600000 % NA Figure 3 1998 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 30.11050 30.1105 0.7318554 8 9 30.05700 30.303 0.7259636 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4657288 302.80 8.571403
897 EI0975 CD005 SI208 2.3200000 % NA Figure 3 1999 Jun Jun Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 28.57500 28.5750 NA 6 6 29.45433 29.453 0.8800009 sum 6 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4503708 302.80 6.112869
898 EI0976 CD005 SI208 2.6400000 % NA Figure 3 2000 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.72050 29.7205 0.2651650 8 9 29.36533 29.813 0.8589678 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6821747 302.80 3.368557
899 EI0977 CD005 SI208 0.9600000 % NA Figure 3 2001 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.73800 29.7380 0.2192027 8 9 29.25034 29.355 0.7008858 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4439392 302.80 0.000000
900 EI0978 CD005 SI208 1.2000000 % NA Figure 3 2002 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.74800 29.7480 0.4313356 8 9 29.33134 29.608 0.8927529 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5910645 302.80 0.000000
901 EI0979 CD005 SI208 5.5200000 % NA Figure 3 2003 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.83150 29.8315 0.2708215 8 9 29.46200 29.540 0.6037899 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8328247 302.80 0.000000
902 EI0980 CD005 SI208 4.0000000 % NA Figure 3 2004 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.66250 29.6625 0.7954951 8 9 29.74267 30.080 0.7135448 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6485596 302.80 0.000000
903 EI0981 CD005 SI208 8.1600000 % NA Figure 3 1998 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 30.11050 30.1105 0.7318554 8 9 30.05700 30.303 0.7259636 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 0.4657288 302.80 8.571403
904 EI0982 CD005 SI208 14.0800000 % NA Figure 3 1999 Jun Jun Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 28.57500 28.5750 NA 6 6 29.45433 29.453 0.8800009 sum 6 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 1.4503708 302.80 6.112869
905 EI0983 CD005 SI208 4.4000000 % NA Figure 3 2000 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.72050 29.7205 0.2651650 8 9 29.36533 29.813 0.8589678 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 1.6821747 302.80 3.368557
906 EI0984 CD005 SI208 0.8000000 % NA Figure 3 2001 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.73800 29.7380 0.2192027 8 9 29.25034 29.355 0.7008858 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 0.4439392 302.80 0.000000
907 EI0985 CD005 SI208 3.1200000 % NA Figure 3 2002 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.74800 29.7480 0.4313356 8 9 29.33134 29.608 0.8927529 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 1.5910645 302.80 0.000000
908 EI0986 CD005 SI208 7.6000000 % NA Figure 3 2003 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.83150 29.8315 0.2708215 8 9 29.46200 29.540 0.6037899 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 0.8328247 302.80 0.000000
909 EI0987 CD005 SI208 1.8400000 % NA Figure 3 2004 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.66250 29.6625 0.7954951 8 9 29.74267 30.080 0.7135448 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 NA 0.6485596 302.80 0.000000
910 EI0988 CD005 SI208 2.8695652 % NA Figure 3 1998 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 30.11050 30.1105 0.7318554 8 9 30.05700 30.303 0.7259636 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4657288 302.80 8.571403
911 EI0989 CD005 SI208 5.1304348 % NA Figure 3 1999 Jun Jun Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 28.57500 28.5750 NA 6 6 29.45433 29.453 0.8800009 sum 6 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4503708 302.80 6.112869
912 EI0990 CD005 SI208 0.8695652 % NA Figure 3 2000 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.72050 29.7205 0.2651650 8 9 29.36533 29.813 0.8589678 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6821747 302.80 3.368557
913 EI0991 CD005 SI208 4.2608696 % NA Figure 3 2001 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.73800 29.7380 0.2192027 8 9 29.25034 29.355 0.7008858 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4439392 302.80 0.000000
914 EI0992 CD005 SI208 1.8260870 % NA Figure 3 2002 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.74800 29.7480 0.4313356 8 9 29.33134 29.608 0.8927529 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5910645 302.80 0.000000
915 EI0993 CD005 SI208 7.4782609 % NA Figure 3 2003 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.83150 29.8315 0.2708215 8 9 29.46200 29.540 0.6037899 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8328247 302.80 0.000000
916 EI0994 CD005 SI208 1.1304348 % NA Figure 3 2004 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.66250 29.6625 0.7954951 8 9 29.74267 30.080 0.7135448 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6485596 302.80 0.000000
917 EI0995 CD005 SI208 3.5915490 % NA Figure 3 1998 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 30.11050 30.1105 0.7318554 8 9 30.05700 30.303 0.7259636 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4657288 302.80 8.571403
918 EI0996 CD005 SI208 4.3661970 % NA Figure 3 1999 Jun Jun Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 28.57500 28.5750 NA 6 6 29.45433 29.453 0.8800009 sum 6 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4503708 302.80 6.112869
919 EI0997 CD005 SI208 1.9718310 % NA Figure 3 2000 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.72050 29.7205 0.2651650 8 9 29.36533 29.813 0.8589678 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6821747 302.80 3.368557
920 EI0998 CD005 SI208 1.0563380 % NA Figure 3 2001 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.73800 29.7380 0.2192027 8 9 29.25034 29.355 0.7008858 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4439392 302.80 0.000000
921 EI0999 CD005 SI208 2.3943660 % NA Figure 3 2002 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.74800 29.7480 0.4313356 8 9 29.33134 29.608 0.8927529 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5910645 302.80 0.000000
922 EI1000 CD005 SI208 4.6478870 % NA Figure 3 2003 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.83150 29.8315 0.2708215 8 9 29.46200 29.540 0.6037899 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8328247 302.80 0.000000
923 EI1001 CD005 SI208 5.0704230 % NA Figure 3 2004 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.66250 29.6625 0.7954951 8 9 29.74267 30.080 0.7135448 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 1 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6485596 302.80 0.000000
924 EI1002 CD005 SI208 1.6901410 % NA Figure 3 1998 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 30.11050 30.1105 0.7318554 8 9 30.05700 30.303 0.7259636 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4657288 302.80 8.571403
925 EI1003 CD005 SI208 1.9718310 % NA Figure 3 1999 Jun Jun Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 28.57500 28.5750 NA 6 6 29.45433 29.453 0.8800009 sum 6 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.4503708 302.80 6.112869
926 EI1004 CD005 SI208 1.0563380 % NA Figure 3 2000 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.72050 29.7205 0.2651650 8 9 29.36533 29.813 0.8589678 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.6821747 302.80 3.368557
927 EI1005 CD005 SI208 1.2676060 % NA Figure 3 2001 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.73800 29.7380 0.2192027 8 9 29.25034 29.355 0.7008858 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.4439392 302.80 0.000000
928 EI1006 CD005 SI208 3.9436620 % NA Figure 3 2002 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.74800 29.7480 0.4313356 8 9 29.33134 29.608 0.8927529 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 1.5910645 302.80 0.000000
929 EI1007 CD005 SI208 1.0563380 % NA Figure 3 2003 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.83150 29.8315 0.2708215 8 9 29.46200 29.540 0.6037899 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.8328247 302.80 0.000000
930 EI1008 CD005 SI208 2.3943660 % NA Figure 3 2004 Aug-Sep Aug-Sep Methods Observational data p1324 Florida Keys and Dry Tortugas Methods Observational data p1324 Caribbean & Gulf of Mexico Caribbean/Atlantic 24.500000 -81.50000 24.500000 -81.50000 Methods Observational data p1324, took middle of each coordinate value NA NA Prevalence extracted using MetaDigitise in Rstudio n = 55 Atlantic Ocean 29.66250 29.6625 0.7954951 8 9 29.74267 30.080 0.7135448 aut 8 summer NA NA NA 17.10500 55 Methods Observational data p1324 NA 0 NA Circle 55 9.0000 NA 311.0000 Methods Observational data p1324 length is radius and averaged (real length = 8-10) 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0.6485596 302.80 0.000000

Data Exploration

Check for Erroneous Values

# Check spread of disease prevalence values and ensure all fall in % ranges
countpercent <- count(rdsdat, Disease_Prevalence)
ggplot(countpercent) +
  geom_histogram(aes(x = Disease_Prevalence), color = "slateblue2", fill = "slateblue2") +
  labs(x = "Disease Prevalence (%)") +
  theme_bw()

Note large number of near 0% values

Disease Prevalence

# How many effect sizes report 0% disease prevalence?
count(rdsdat, Disease_Prevalence == 0)
##   Disease_Prevalence == 0   n
## 1                   FALSE 794
## 2                    TRUE 124
# How many effect sizes report disease prevalence as a %?
count(ESD, Unit_Prevalence)
## # A tibble: 5 × 2
##   Unit_Prevalence           n
##   <chr>                 <int>
## 1 %                       932
## 2 col/100m^2               67
## 3 col/m^2                  14
## 4 mean no. colonies/m^2     3
## 5 no. col                  11

Sample Areas

# Check spread of survey area values
areaonly <- subset(rdsdat, Sample_Area_km2!="")
countarea <- count(areaonly, Sample_Area_km2)
ggplot(areaonly) +
  geom_histogram(aes(x = Sample_Area_km2), color = "slateblue2", fill = "slateblue2")

# Scale survey area for better visualization
ggplot(areaonly) +
  geom_histogram(aes(x = Sample_Area_km2), color = "slateblue2", fill = "slateblue2")+
  scale_x_log10()

Region Data

# Count data per region
# count(rdsdat, Region_HoeghGuldberg)
# count(rdsdat, Region_Kleypas)
# ggplot(rdsdat) +
#   geom_bar(aes(x = Region_HoeghGuldberg))

ggplot(rdsdat) +
  geom_bar(aes(x = Ocean, color = Ocean, fill = Ocean)) +
  scale_color_viridis_d(end = 0.9) +
  scale_fill_viridis_d(end = 0.9) +
  theme_bw(base_size = 14) +
  theme(legend.position = "bottom")

Year

# Which years have the most data?
ggplot(rdsdat) +
  geom_bar(aes(x = Year), color = "slateblue2", fill = "slateblue2") +
  theme_bw()

Survey Method

# What are the most common sampling methods?
Transect_narm <- subset(rdsdat, is.na(rdsdat$Transect_Type) != TRUE)
ggplot(Transect_narm) +
  geom_bar(aes(x = Transect_Type), color = "slateblue2", fill = "slateblue2") +
  labs(x = "Transect Plot Type") +
  theme_bw()

Map Visualizations

# Prototyping code - see Figure 1A for map

# Set map of world from existing map data
earth <- map_data("world")

# Plot map of survey locations on map
ggplot(rdsdat, aes(x = Lon, y = Lat)) +
  geom_map(data = earth,
           map = earth,
           aes(x = long, y = lat, group = group, map_id = region),
           fill = "white",
           colour = "black",
           size = 0.2) +
  geom_point(colour = 'blue', alpha = 0.25) +
  coord_quickmap()


# Plot survey locations colored by ocean
ggplot(rdsdat, aes(x = Lon, y = Lat, col = Ocean)) +
  geom_map(data = earth, 
           map = earth, 
           aes(x = long, y = lat, group = group, map_id = region), 
           fill = "grey85", 
           colour = "black", 
           size = 0.2) +
  geom_point(alpha = 0.25) +
  coord_quickmap() +
  labs(x = "Longitude", y = "Latitude") +
  theme_bw()


# Try plotting survey locations by disease number found at each site
# Too many values for number of disease. Try merging to just one vs more than one disease per effect size

# Write function to identify the value of Disease_Num
num_dis <- function(Disease_Num) {
  case_when(
    Disease_Num == 1   ~ "One Disease",
    Disease_Num != 1   ~ "Multiple Diseases"
  )
}

# Apply function to dataset
as.DiseaseNum <- rdsdat %>% mutate(type = num_dis(Disease_Num))

# Plot survey locations on map, colored by one or multiple diseases
ggplot(as.DiseaseNum, aes(x = Lon, y = Lat, col = type)) +
  geom_map(data = earth, map = earth, aes(x = long, y = lat, group = group, map_id = region), fill = "white", colour = "black", size = 0.2) +
  geom_point(alpha = 0.25) +
  coord_quickmap()

Phylogenetic Tree

# Prototyping code - see Figure S7 for tree

genus <- unique(prevSPP$Genus[prevSPP$Genus != "Helioseris"]) # Removed the Genus Helioseris because it was removed from the analysed dataset because of the disease prevalence metric used
taxa_list <- na.omit(genus)
taxa_list2 <- as.factor(taxa_list)
taxa <- tnrs_match_names(names = levels(taxa_list2), context_name = "Animals") # Connect the list of Genera with the Open Tree taxonomy

taxa.in.tree <- ott_id(taxa)[is_in_tree(ott_id(taxa))] 

tree <- tol_induced_subtree(ott_ids = taxa.in.tree, label_format = "name")
tree$tip.label <- gsub("_\\(.*","", tree$tip.label) # Remove symbols

plain_tree <- plot(tree, cex = 0.5, label.offset = 0.1, no.margin = TRUE) 

is.binary(tree) # Check if binary
tree <- compute.brlen(tree, method = "Grafen", power = 1) # Compute branch lengths 

all_taxa <- intersect(as.character(tree$tip.label), taxa_list) # Find synonyms and typos and remove them
used_taxa <- setdiff(as.character(tree$tip.label), taxa_list)

# Build heatmap of oceans
species <- select(prevSPP, "PaperID", "Genus")
species$Paper_ID <- species$PaperID
dat.tree <- left_join(rdsdat, species, by = c("Paper_ID")) # Join the data with the Genus information
dat.tree <- select(dat.tree, Paper_ID, Genus, Ocean) # Reduce the data to the needed columns
dat.tree <- mutate(dat.tree, search_string = decapitalize(Genus)) # Decapitalize "Genus" to match the "search string" in the "taxa" file
dat.tree <- left_join(dat.tree, select(taxa, search_string, unique_name, ott_id), by = "search_string") # Join taxa information from rotl to the dataset

dat.tree$unique_name <- word(dat.tree$unique_name, 1) # Only keep one word for the Genus (we had e.g. "Stylophora (genus in phylum Cnidaria)")

dat.tree <- dat.tree[dat.tree$unique_name %in% tree$tip.label, ] # Check that the Genera match the ones used in the tree

ocean <- dat.tree %>% group_by(unique_name) %>% summarise( 
  oceanIndian = Ocean == "Indian Ocean", 
  oceanAtlantic = Ocean == "Atlantic Ocean",
  oceanPacific = Ocean == "Pacific Ocean")
ocean <- distinct(ocean) # Only keep unique rows
ocean$oceanIndian = as.numeric(ocean$oceanIndian) # convert TRUE/FALSE to binary values for each Ocean
ocean$oceanAtlantic = as.numeric(ocean$oceanAtlantic)
ocean$oceanPacific = as.numeric(ocean$oceanPacific)

ocean <- ocean %>% group_by(unique_name) %>% summarise(oceanIndian=sum(oceanIndian), # calculate the sum for each species (i.e., if 1, the species is found in the designated ocean)
                                                     oceanAtlantic=sum(oceanAtlantic),
                                                     oceanPacific=sum(oceanPacific))
ocean$oceanIndian=as.factor(ocean$oceanIndian) # Convert back to factor for the plot
ocean$oceanAtlantic=as.factor(ocean$oceanAtlantic)
ocean$oceanPacific=as.factor(ocean$oceanPacific)

# Plot Horizontal Tree
# simple phylogenetic tree
htree <- ggtree(tree, lwd = 1.05) + 
  geom_tiplab(offset = 0.04) # Display Genus

h <- htree %<+% ocean # Link plot to data

# plot tree and heatmap together
h2 <- h +  geom_fruit(geom=geom_tile, 
                     mapping=aes(fill=oceanAtlantic), 
                     width=0.075, 
                     offset=0.35) + 
  scale_fill_manual(name = "", values=c("white", "#7C4F85"), labels=c("", "Atlantic Ocean"), guide = guide_legend(order = 1)) +
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanIndian), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white","#71A8AF"), labels=c("", "Indian Ocean"), guide = guide_legend(order = 2))+
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanPacific), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white", "#D4E974"), labels=c("", "Pacific Ocean"), guide = guide_legend(order = 3))

h2

Disease Number and Types

Spread of disease data

# Visualize spread of disease number data
ggplot(rdsdat) +
  geom_bar(aes(x = Disease_Num), color = "slateblue2", fill = "slateblue2") +
  labs(x = "Disease Number") +
  theme_bw()

# Plot spread of effect sizes that report one or many diseases
ggplot() +
  geom_bar(rdsdat, mapping = aes(x = Disease_Num == 1), color = "slateblue2", fill = "slateblue2") +
  theme_bw() +
  labs(x = "Disease Number") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Multiple Diseases', 'One Disease'))

# summarize data by disease

# create dataset
Disease_Counts <- data.frame(WS = count(rdsdat, WS == 1)[2,2],
                             BBD = count(rdsdat, BBD == 1)[2,2],
                             GA = count(rdsdat, GA == 1)[2,2],
                             BrB = count(rdsdat, BrB == 1)[2,2],
                             SEB = count(rdsdat, SEB == 1)[2,2],
                             UWS = count(rdsdat, UWS == 1)[2,2],
                             TL = count(rdsdat, TL == 1)[2,2],
                             DSS = count(rdsdat, DSS == 1)[2,2],
                             WB = count(rdsdat, WB == 1)[2,2],
                             YBD = count(rdsdat, YBD == 1)[2,2],
                             WPx = count(rdsdat, WPx == 1)[2,2],
                             IMS = count(rdsdat, IMS == 1)[2,2],
                             Trema = count(rdsdat, Trema == 1)[2,2],
                             Cyano = count(rdsdat, Cyano == 1)[2,2],
                             PS = count(rdsdat, PS == 1)[2,2],
                             AN = count(rdsdat, AN == 1)[2,2],
                             PR = count(rdsdat, PR == 1)[2,2],
                             PUWS = count(rdsdat, PUWS == 1)[2,2],
                             DWS = count(rdsdat, DWS == 1)[2,2],
                             RBD = count(rdsdat, RBD == 1)[2,2],
                             STGA = count(rdsdat, STGA == 1)[2,2],
                             RM = count(rdsdat, RM == 1)[2,2],
                             RW = count(rdsdat, RW == 1)[2,2],
                             PLS = count(rdsdat, PLS == 1)[2,2],
                             PWPS = count(rdsdat, PWPS == 1)[2,2],
                             CT = count(rdsdat, CT == 1)[2,2],
                             PBTL = count(rdsdat, PBTL == 1)[2,2],
                             WPa = count(rdsdat, WPa == 1)[2,2],
                             Cilia = count(rdsdat, Cilia == 1)[2,2],
                             PBSS = count(rdsdat, PBSS == 1)[2,2],
                             GPD = count(rdsdat, GPD == 1)[2,2],
                             Unk = count(rdsdat, Unknown == 1)[2,2])

Most common diseases by ocean

#isolate data per ocean
Atlantic_Only <- subset(rdsdat, rdsdat$Ocean == "Atlantic Ocean")
Pacific_Only <- subset(rdsdat, rdsdat$Ocean == "Pacific Ocean")
Indian_Only <- subset(rdsdat, rdsdat$Ocean == "Indian Ocean")

White Syndrome

WS_Atl <- ggplot() +
  geom_bar(Atlantic_Only, mapping = aes(x = Atlantic_Only$WS == 1), color = "grey80", fill = "grey80") +
  theme_bw() +
  labs(title = "Atlantic Ocean", x = "White Syndrome") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
WS_Pac <- ggplot() +
  geom_bar(Pacific_Only, mapping = aes(x = Pacific_Only$WS == 1), color = "grey80", fill = "grey80") +
  theme_bw() +
  labs(title = "Pacific Ocean", x = "White Syndrome") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
WS_Ind <- ggplot() +
  geom_bar(Indian_Only, mapping = aes(x = Indian_Only$WS == 1), color = "grey80", fill = "grey80") +
  theme_bw() +
  labs(title = "Indian Ocean", x = "White Syndrome") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
WS_Atl + WS_Pac + WS_Ind

Black Band Disease

BBD_Atl <- ggplot() +
  geom_bar(Atlantic_Only, mapping = aes(x = Atlantic_Only$BBD == 1), color = "black", fill = "black") +
  theme_bw() +
  labs(title = "Atlantic Ocean", x = "Black Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
BBD_Pac <- ggplot() +
  geom_bar(Pacific_Only, mapping = aes(x = Pacific_Only$BBD == 1), color = "black", fill = "black") +
  theme_bw() +
  labs(title = "Pacific Ocean", x = "Black Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
BBD_Ind <- ggplot() +
  geom_bar(Indian_Only, mapping = aes(x = Indian_Only$BBD == 1), color = "black", fill = "black") +
  theme_bw() +
  labs(title = "Indian Ocean", x = "Black Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
BBD_Atl + BBD_Pac + BBD_Ind

Yellow Band Disease

YBD_Atl <- ggplot() +
  geom_bar(Atlantic_Only, mapping = aes(x = Atlantic_Only$YBD == 1), color = "yellow", fill = "yellow") +
  theme_bw() +
  labs(title = "Atlantic Ocean", x = "Yellow Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
YBD_Pac <- ggplot() +
  geom_bar(Pacific_Only, mapping = aes(x = Pacific_Only$YBD == 1), color = "yellow", fill = "yellow") +
  theme_bw() +
  labs(title = "Pacific Ocean", x = "Yellow Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
YBD_Ind <- ggplot() +
  geom_bar(Indian_Only, mapping = aes(x = Indian_Only$YBD == 1), color = "yellow", fill = "yellow") +
  theme_bw() +
  labs(title = "Indian Ocean", x = "Yellow Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
YBD_Atl + YBD_Pac + YBD_Ind

### {-}

Hemisphere data

North Hemisphere

# prototyping code

# Isolate North hemisphere values
poslat <- subset(rdsdat, Lat > "0") # Need to split into proper month format rather than the written format

# Table of North hemisphere values
table(poslat$start_month) -> Ncounts
# change table to dataframe
Ncounts <- as.data.frame(t(Ncounts))
# rename values to actual month name for axis values
Ncounts$Var2 <- c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')
# rename column for axis label
names(Ncounts)[2] <- 'Month'

# plot count of survey by start month in north
Nhemi <- ggplot(Ncounts) +
  geom_bar(aes(x=Month, y=Freq), color = "darkorchid3", fill = "darkorchid3", stat = 'identity') +
  theme_bw() +
  scale_x_discrete(limits = c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')) +
  labs(y = "North Freq") +
  theme(text = element_text(size = 15))
Nhemi

South Hemisphere

# prototyping code

# Isolate South hemisphere values
neglat <- subset(rdsdat, Lat < "0")

# Table of South hemisphere values
Scounts <- table(neglat$start_month)
# Change table to dataframe
Scounts <- as.data.frame(t(Scounts))
# Rename values to actual month name for axis values
Scounts$Var2 <- c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')
# Rename column for axis label
names(Scounts)[2] <- 'Month'

# plot count of survey by start month in south
Shemi <- ggplot(Scounts) +
  geom_bar(aes(x=Month, y=Freq), color = "gold2", fill = "gold2", stat = 'identity') +
  theme(text = element_text(size = 50)) +
  theme_bw() +
  scale_y_reverse() +
  scale_x_discrete(limits = c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'), position = "top") +
  labs(y = "South Freq")
Shemi

Mirror Hemispheres

# prototyping code

# Mirror view of Hemispheres
Nhemi/Shemi

Meta-Analytic Models

Scale and Center Data

# Change percentage to a proportion
rdsdat$Disease_P <-  (rdsdat$Disease_Prevalence)/100

# Scale predictors
rdsdat$sYear <- scale(rdsdat$Year)
rdsdat$sSumTemp <- scale(rdsdat$average_SST_summer)
rdsdat$sWSSTA <- scale(rdsdat$WSSTA)
rdsdat$sDisease_Num <- scale(rdsdat$Disease_Num)
rdsdat$logArea <- log(rdsdat$Sample_Area_km2*10e5)

# Center predictors
rdsdat$cYear <- scale(rdsdat$Year, scale = F)
rdsdat$cSumTemp <- scale(rdsdat$average_SST_summer, scale = F)

Run Models

These models take very long to run and are too big to send through to GitHub. We have provided these models for download here.

Model with no interaction between key variables (WSSTA, SST, and Year)

no_interaction_f <- brmsformula(
  Disease_P|weights(logArea) ~ # estimates weighed by the logarithm of the sample area
    sYear + # Scaled sample year
    sSumTemp +  # Scaled summer temperature
    sWSSTA + # Scaled weekly sea surface temperature anomaly
    sDisease_Num + # Scaled number of diseases
    Ocean + # Ocean basin
    (1| Site_ID) + # Site as a random factor
    (1|Paper_ID) + # Study as a random factor
    (1|season) + # Season as a random factor
    (1|Transect_Type), # Type of transect as a random factor
  zi ~ 1 + sSumTemp + sWSSTA + sYear, # zi = zero-inflation
  phi ~ 1 + sSumTemp + sWSSTA + sYear) # phi = heteroscedasticity

no_interaction <- brm(no_interaction_f,
                      chains = 2,
                      iter = 30000,
                      warmup = 28000,
                      data = rdsdat,
                      family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                      control = list(adapt_delta = 0.95))
no_interaction <- readRDS(here("Rdata","no_interaction_mod.rds"))

summary(no_interaction)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear + sSumTemp + sWSSTA + sDisease_Num + Ocean + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.30      0.12     1.07     1.55 1.00     1721     2254
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.88      0.48     0.36     2.20 1.00     3434     3070
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.62      0.06     0.52     0.75 1.00     1138     2106
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.76      0.47     0.24     1.97 1.00     2807     2743
## 
## Population-Level Effects: 
##                   Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept            -2.36      0.65    -3.60    -1.01 1.00     2217     2537
## phi_Intercept         2.89      0.02     2.86     2.93 1.00     9975     2544
## zi_Intercept         -1.90      0.03    -1.95    -1.84 1.00    11210     2743
## sYear                 0.25      0.03     0.19     0.31 1.00    10128     2786
## sSumTemp              0.28      0.04     0.21     0.35 1.00     8854     2963
## sWSSTA                0.20      0.02     0.16     0.23 1.00     9039     3053
## sDisease_Num          0.38      0.11     0.16     0.60 1.00     2426     2862
## OceanIndianOcean     -0.42      0.38    -1.15     0.33 1.00     1927     2743
## OceanPacificOcean    -0.31      0.31    -0.94     0.28 1.00     1732     2535
## phi_sSumTemp         -0.25      0.01    -0.27    -0.22 1.00    10035     2743
## phi_sWSSTA           -0.24      0.01    -0.26    -0.22 1.00     9424     3468
## phi_sYear             0.31      0.02     0.28     0.34 1.00     8156     2969
## zi_sSumTemp           0.13      0.03     0.08     0.19 1.00    12415     2494
## zi_sWSSTA            -0.17      0.03    -0.24    -0.11 1.00     9507     2642
## zi_sYear             -0.03      0.03    -0.08     0.02 1.00    13090     3053
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interactions between all key variables and Ocean

all_interaction_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp*Ocean + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

all_interaction <- brm(all_interaction_f,
                       chains = 2,
                       iter = 30000,
                       warmup = 28000,
                       data = rdsdat,
                       family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                       control = list(adapt_delta = 0.95))
all_interaction <- readRDS(here("Rdata","all_interaction_mod.rds"))

summary(all_interaction)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear * Ocean + sSumTemp * Ocean + sWSSTA * Ocean + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.31      0.12     1.09     1.57 1.00     1983     2646
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.89      0.49     0.37     2.22 1.00     2999     2718
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.61      0.06     0.51     0.73 1.00     1347     2390
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.75      0.44     0.23     1.96 1.00     3142     2871
## 
## Population-Level Effects: 
##                            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                     -2.35      0.64    -3.59    -0.98 1.00     2135
## phi_Intercept                  2.90      0.02     2.87     2.93 1.00     6038
## zi_Intercept                  -1.90      0.03    -1.95    -1.84 1.00     7062
## sYear                          0.24      0.03     0.17     0.31 1.00     6835
## OceanIndianOcean              -0.11      0.44    -0.97     0.76 1.00     2049
## OceanPacificOcean             -0.54      0.34    -1.20     0.11 1.00     2212
## sSumTemp                       0.31      0.04     0.23     0.39 1.00     6061
## sWSSTA                         0.20      0.02     0.16     0.24 1.00     6329
## sDisease_Num                   0.38      0.11     0.16     0.61 1.00     2919
## sYear:OceanIndianOcean        -0.34      0.26    -0.83     0.17 1.00     5001
## sYear:OceanPacificOcean        0.24      0.13    -0.01     0.50 1.00     5838
## OceanIndianOcean:sSumTemp      0.07      0.11    -0.16     0.29 1.00     4575
## OceanPacificOcean:sSumTemp    -0.24      0.10    -0.43    -0.05 1.00     5369
## OceanIndianOcean:sWSSTA       -0.02      0.05    -0.12     0.08 1.00     4604
## OceanPacificOcean:sWSSTA      -0.02      0.08    -0.18     0.15 1.00     4363
## phi_sSumTemp                  -0.25      0.01    -0.28    -0.22 1.00     6492
## phi_sWSSTA                    -0.24      0.01    -0.26    -0.22 1.00     6074
## phi_sYear                      0.31      0.02     0.28     0.34 1.00     6420
## zi_sSumTemp                    0.13      0.03     0.08     0.19 1.00     5933
## zi_sWSSTA                     -0.17      0.03    -0.24    -0.11 1.00     6136
## zi_sYear                      -0.03      0.03    -0.08     0.02 1.00     7073
##                            Tail_ESS
## Intercept                      2254
## phi_Intercept                  2649
## zi_Intercept                   2815
## sYear                          2711
## OceanIndianOcean               2635
## OceanPacificOcean              2428
## sSumTemp                       3471
## sWSSTA                         3466
## sDisease_Num                   2730
## sYear:OceanIndianOcean         2856
## sYear:OceanPacificOcean        2889
## OceanIndianOcean:sSumTemp      2778
## OceanPacificOcean:sSumTemp     2760
## OceanIndianOcean:sWSSTA        3231
## OceanPacificOcean:sWSSTA       2569
## phi_sSumTemp                   2967
## phi_sWSSTA                     3273
## phi_sYear                      3448
## zi_sSumTemp                    2628
## zi_sWSSTA                      2911
## zi_sYear                       3147
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interaction between Year and Ocean and SST and Ocean only

YearxOcean_SSTxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp*Ocean + 
    sWSSTA +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

YearxOcean_SSTxOcean <- brm(YearxOcean_SSTxOcean_f,
                            chains = 2,
                            iter = 30000,
                            warmup = 28000,
                            data = rdsdat,
                            family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                            control = list(adapt_delta = 0.95))
YearxOcean_SSTxOcean <- readRDS(here("Rdata","YearxOcean_SSTxOcean_mod.rds"))

summary(YearxOcean_SSTxOcean)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear * Ocean + sSumTemp * Ocean + sWSSTA + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.32      0.12     1.09     1.57 1.00     1689     2325
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.89      0.47     0.37     2.13 1.00     3916     2852
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.61      0.06     0.51     0.73 1.00     1422     2433
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.76      0.47     0.23     2.01 1.00     3784     2886
## 
## Population-Level Effects: 
##                            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                     -2.35      0.61    -3.52    -1.06 1.00     2695
## phi_Intercept                  2.90      0.02     2.87     2.93 1.00     7936
## zi_Intercept                  -1.89      0.03    -1.95    -1.84 1.00    10078
## sYear                          0.24      0.03     0.17     0.30 1.00     7908
## OceanIndianOcean              -0.09      0.43    -0.92     0.76 1.00     2490
## OceanPacificOcean             -0.53      0.33    -1.19     0.10 1.00     2053
## sSumTemp                       0.31      0.04     0.23     0.38 1.00     7310
## sWSSTA                         0.20      0.02     0.16     0.23 1.00     8137
## sDisease_Num                   0.38      0.11     0.17     0.60 1.00     2877
## sYear:OceanIndianOcean        -0.38      0.23    -0.81     0.07 1.00     7243
## sYear:OceanPacificOcean        0.24      0.12     0.01     0.48 1.00     6466
## OceanIndianOcean:sSumTemp      0.06      0.11    -0.16     0.28 1.00     7837
## OceanPacificOcean:sSumTemp    -0.24      0.10    -0.43    -0.05 1.00     5905
## phi_sSumTemp                  -0.25      0.01    -0.28    -0.22 1.00     8819
## phi_sWSSTA                    -0.24      0.01    -0.26    -0.22 1.00     7725
## phi_sYear                      0.31      0.02     0.28     0.34 1.00     7588
## zi_sSumTemp                    0.13      0.03     0.08     0.19 1.00    11260
## zi_sWSSTA                     -0.17      0.03    -0.24    -0.11 1.00     9420
## zi_sYear                      -0.03      0.03    -0.08     0.02 1.00    10302
##                            Tail_ESS
## Intercept                      2740
## phi_Intercept                  2553
## zi_Intercept                   2645
## sYear                          3497
## OceanIndianOcean               3032
## OceanPacificOcean              2543
## sSumTemp                       3403
## sWSSTA                         3086
## sDisease_Num                   2754
## sYear:OceanIndianOcean         3051
## sYear:OceanPacificOcean        3150
## OceanIndianOcean:sSumTemp      2916
## OceanPacificOcean:sSumTemp     2914
## phi_sSumTemp                   3127
## phi_sWSSTA                     3265
## phi_sYear                      3411
## zi_sSumTemp                    2438
## zi_sWSSTA                      2591
## zi_sYear                       2902
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interaction between SST and Ocean and WSSTA and Ocean only

SSTxOcean_WSSTAxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear +
    sSumTemp*Ocean + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

SSTxOcean_WSSTAxOcean <- brm(SSTxOcean_WSSTAxOcean_f,
                             chains = 2,
                             iter = 30000,
                             warmup = 28000,
                             data = rdsdat,
                             family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                             control = list(adapt_delta = 0.95))
SSTxOcean_WSSTAxOcean <- readRDS(here("Rdata","SSTxOcean_WSSTAxOcean_mod.rds"))

summary(SSTxOcean_WSSTAxOcean)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear + sSumTemp * Ocean + sWSSTA * Ocean + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.30      0.12     1.08     1.56 1.00     2110     2863
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.88      0.46     0.37     2.05 1.00     4022     2772
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.62      0.06     0.52     0.74 1.00     1338     2344
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.72      0.43     0.23     1.89 1.00     3409     2928
## 
## Population-Level Effects: 
##                            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                     -2.37      0.62    -3.54    -1.04 1.00     3433
## phi_Intercept                  2.89      0.02     2.86     2.92 1.00     5428
## zi_Intercept                  -1.90      0.03    -1.95    -1.84 1.00     5982
## sYear                          0.25      0.03     0.18     0.32 1.00     6784
## sSumTemp                       0.31      0.04     0.23     0.38 1.00     5413
## OceanIndianOcean              -0.45      0.39    -1.20     0.30 1.00     2893
## OceanPacificOcean             -0.41      0.32    -1.05     0.22 1.00     2794
## sWSSTA                         0.20      0.02     0.16     0.24 1.00     5710
## sDisease_Num                   0.38      0.11     0.16     0.60 1.00     4439
## sSumTemp:OceanIndianOcean      0.08      0.12    -0.15     0.32 1.00     6799
## sSumTemp:OceanPacificOcean    -0.23      0.09    -0.42    -0.04 1.00     6849
## OceanIndianOcean:sWSSTA       -0.05      0.05    -0.14     0.04 1.00     5723
## OceanPacificOcean:sWSSTA       0.02      0.08    -0.14     0.18 1.00     5666
## phi_sSumTemp                  -0.25      0.01    -0.27    -0.22 1.00     5866
## phi_sWSSTA                    -0.24      0.01    -0.26    -0.22 1.00     5898
## phi_sYear                      0.31      0.02     0.28     0.34 1.00     5752
## zi_sSumTemp                    0.13      0.03     0.08     0.19 1.00     5757
## zi_sWSSTA                     -0.17      0.03    -0.24    -0.10 1.00     5287
## zi_sYear                      -0.03      0.03    -0.08     0.02 1.00     5527
##                            Tail_ESS
## Intercept                      2568
## phi_Intercept                  2646
## zi_Intercept                   2717
## sYear                          3128
## sSumTemp                       3528
## OceanIndianOcean               2915
## OceanPacificOcean              2600
## sWSSTA                         3009
## sDisease_Num                   2706
## sSumTemp:OceanIndianOcean      2718
## sSumTemp:OceanPacificOcean     2959
## OceanIndianOcean:sWSSTA        3247
## OceanPacificOcean:sWSSTA       2696
## phi_sSumTemp                   2928
## phi_sWSSTA                     2964
## phi_sYear                      3251
## zi_sSumTemp                    2841
## zi_sWSSTA                      3015
## zi_sYear                       2630
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interaction between Year and Ocean and WSSTA and Ocean only

YearxOcean_WSSTAxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

YearxOcean_WSSTAxOcean <- brm(YearxOcean_WSSTAxOcean_f,
                              chains = 2,
                              iter = 30000,
                              warmup = 28000,
                              data = rdsdat,
                              family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                              control = list(adapt_delta = 0.95))
YearxOcean_WSSTAxOcean <- readRDS(here("Rdata","YearxOcean_WSSTAxOcean_mod.rds"))

summary(YearxOcean_WSSTAxOcean)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear * Ocean + sSumTemp + sWSSTA * Ocean + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.32      0.13     1.09     1.60 1.00     2053     2600
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.87      0.47     0.36     2.14 1.00     3430     2714
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.63      0.06     0.53     0.75 1.00     1489     2369
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.76      0.44     0.25     1.95 1.00     2854     2929
## 
## Population-Level Effects: 
##                          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                   -2.37      0.63    -3.56    -1.04 1.00     2817
## phi_Intercept                2.90      0.02     2.87     2.93 1.00     5906
## zi_Intercept                -1.90      0.03    -1.95    -1.84 1.00     7012
## sYear                        0.24      0.03     0.18     0.31 1.00     6344
## OceanIndianOcean            -0.10      0.45    -1.02     0.76 1.00     2928
## OceanPacificOcean           -0.46      0.33    -1.12     0.20 1.00     2529
## sSumTemp                     0.28      0.03     0.21     0.35 1.00     6097
## sWSSTA                       0.20      0.02     0.16     0.25 1.00     6970
## sDisease_Num                 0.39      0.11     0.18     0.62 1.00     3543
## sYear:OceanIndianOcean      -0.36      0.26    -0.86     0.14 1.00     5552
## sYear:OceanPacificOcean      0.23      0.13    -0.02     0.48 1.00     5473
## OceanIndianOcean:sWSSTA     -0.01      0.05    -0.11     0.09 1.00     5291
## OceanPacificOcean:sWSSTA    -0.02      0.09    -0.20     0.15 1.00     5098
## phi_sSumTemp                -0.25      0.01    -0.28    -0.22 1.00     6224
## phi_sWSSTA                  -0.24      0.01    -0.26    -0.22 1.00     6414
## phi_sYear                    0.31      0.02     0.28     0.34 1.00     6254
## zi_sSumTemp                  0.13      0.03     0.08     0.19 1.00     6401
## zi_sWSSTA                   -0.17      0.03    -0.24    -0.11 1.00     6099
## zi_sYear                    -0.03      0.03    -0.08     0.02 1.00     5735
##                          Tail_ESS
## Intercept                    2571
## phi_Intercept                2761
## zi_Intercept                 2897
## sYear                        3034
## OceanIndianOcean             2208
## OceanPacificOcean            2402
## sSumTemp                     2815
## sWSSTA                       3548
## sDisease_Num                 2779
## sYear:OceanIndianOcean       2597
## sYear:OceanPacificOcean      2931
## OceanIndianOcean:sWSSTA      3149
## OceanPacificOcean:sWSSTA     3023
## phi_sSumTemp                 3025
## phi_sWSSTA                   3242
## phi_sYear                    2639
## zi_sSumTemp                  2727
## zi_sWSSTA                    2570
## zi_sYear                     2625
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interaction between Year and Ocean only

YearxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp + 
    sWSSTA +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

YearxOcean <- brm(YearxOcean_f,
                  chains = 2,
                  iter = 30000,
                  warmup = 28000,
                  data = rdsdat,
                  family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                  control = list(adapt_delta = 0.95))
YearxOcean <- readRDS(here("Rdata","YearxOcean_mod.rds"))

summary(YearxOcean)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear * Ocean + sSumTemp + sWSSTA + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.32      0.13     1.09     1.59 1.00     2081     2934
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.87      0.46     0.37     2.04 1.00     4605     3179
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.62      0.06     0.52     0.75 1.00     1505     2625
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.76      0.44     0.25     1.89 1.00     3428     3257
## 
## Population-Level Effects: 
##                         Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                  -2.38      0.63    -3.62    -1.06 1.00     3721
## phi_Intercept               2.90      0.02     2.87     2.93 1.00     5443
## zi_Intercept               -1.90      0.03    -1.95    -1.84 1.00     6448
## sYear                       0.24      0.03     0.18     0.31 1.00     6726
## OceanIndianOcean           -0.08      0.43    -0.91     0.77 1.00     3616
## OceanPacificOcean          -0.45      0.33    -1.08     0.20 1.00     3027
## sSumTemp                    0.28      0.03     0.21     0.35 1.00     7135
## sWSSTA                      0.20      0.02     0.17     0.24 1.00     6629
## sDisease_Num                0.38      0.12     0.16     0.62 1.00     3923
## sYear:OceanIndianOcean     -0.38      0.23    -0.83     0.06 1.00     8478
## sYear:OceanPacificOcean     0.22      0.12    -0.01     0.47 1.00     7331
## phi_sSumTemp               -0.25      0.01    -0.28    -0.22 1.00     7273
## phi_sWSSTA                 -0.24      0.01    -0.26    -0.22 1.00     6788
## phi_sYear                   0.31      0.02     0.28     0.34 1.00     6340
## zi_sSumTemp                 0.13      0.03     0.08     0.19 1.00     6693
## zi_sWSSTA                  -0.17      0.03    -0.24    -0.11 1.00     5946
## zi_sYear                   -0.03      0.03    -0.08     0.02 1.00     6088
##                         Tail_ESS
## Intercept                   2763
## phi_Intercept               2853
## zi_Intercept                2763
## sYear                       3095
## OceanIndianOcean            3121
## OceanPacificOcean           2562
## sSumTemp                    2996
## sWSSTA                      3555
## sDisease_Num                2916
## sYear:OceanIndianOcean      2933
## sYear:OceanPacificOcean     3060
## phi_sSumTemp                3172
## phi_sWSSTA                  2947
## phi_sYear                   3130
## zi_sSumTemp                 2998
## zi_sWSSTA                   2795
## zi_sYear                    2489
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interaction between WSSTA and Ocean only

WSSTAxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear +
    sSumTemp + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

WSSTAxOcean <- brm(WSSTAxOcean_f,
                   chains = 2,
                   iter = 30000,
                   warmup = 28000,
                   data = rdsdat,
                   family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                   control = list(adapt_delta = 0.95))
WSSTAxOcean <- readRDS(here("Rdata","WSSTAxOcean_mod.rds"))

summary(WSSTAxOcean)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear + sSumTemp + sWSSTA * Ocean + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.30      0.12     1.08     1.55 1.00     1567     2260
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.87      0.46     0.36     2.13 1.00     2697     2654
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.63      0.06     0.53     0.75 1.00     1286     1954
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.74      0.43     0.24     1.88 1.00     2422     2378
## 
## Population-Level Effects: 
##                          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                   -2.38      0.58    -3.51    -1.17 1.00     2099
## phi_Intercept                2.89      0.02     2.86     2.93 1.00     6891
## zi_Intercept                -1.90      0.03    -1.95    -1.84 1.00     7268
## sYear                        0.25      0.03     0.19     0.32 1.00     6553
## sSumTemp                     0.28      0.03     0.21     0.34 1.00     6139
## sWSSTA                       0.21      0.02     0.17     0.24 1.00     5929
## OceanIndianOcean            -0.45      0.38    -1.19     0.30 1.00     1371
## OceanPacificOcean           -0.33      0.32    -0.92     0.29 1.00     1519
## sDisease_Num                 0.37      0.11     0.16     0.60 1.00     2152
## sWSSTA:OceanIndianOcean     -0.05      0.04    -0.13     0.04 1.00     6267
## sWSSTA:OceanPacificOcean     0.02      0.08    -0.14     0.18 1.00     3816
## phi_sSumTemp                -0.25      0.01    -0.27    -0.22 1.00     7532
## phi_sWSSTA                  -0.24      0.01    -0.26    -0.22 1.00     6285
## phi_sYear                    0.31      0.02     0.28     0.34 1.00     6616
## zi_sSumTemp                  0.13      0.03     0.08     0.19 1.00     7135
## zi_sWSSTA                   -0.17      0.03    -0.24    -0.10 1.00     6327
## zi_sYear                    -0.03      0.03    -0.08     0.02 1.00     6851
##                          Tail_ESS
## Intercept                    2491
## phi_Intercept                2809
## zi_Intercept                 2814
## sYear                        3029
## sSumTemp                     2867
## sWSSTA                       3489
## OceanIndianOcean             1926
## OceanPacificOcean            2257
## sDisease_Num                 2538
## sWSSTA:OceanIndianOcean      3213
## sWSSTA:OceanPacificOcean     2696
## phi_sSumTemp                 2533
## phi_sWSSTA                   3174
## phi_sYear                    2810
## zi_sSumTemp                  2712
## zi_sWSSTA                    2354
## zi_sYear                     2714
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model with interaction between SST and Ocean only

SSTxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear +
    sSumTemp*Ocean + 
    sWSSTA +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

SSTxOcean <- brm(SSTxOcean_f,
                 chains = 2,
                 iter = 30000,
                 warmup = 28000,
                 data = rdsdat,
                 family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                 control = list(adapt_delta = 0.95))
SSTxOcean <- readRDS(here("Rdata","SSTxOcean_mod.rds"))

summary(SSTxOcean)
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = log; zi = logit 
## Formula: Disease_P | weights(logArea) ~ sYear + sSumTemp * Ocean + sWSSTA + sDisease_Num + (1 | Site_ID) + (1 | Paper_ID) + (1 | season) + (1 | Transect_Type) 
##          zi ~ 1 + sSumTemp + sWSSTA + sYear
##          phi ~ 1 + sSumTemp + sWSSTA + sYear
##    Data: rdsdat (Number of observations: 918) 
##   Draws: 2 chains, each with iter = 30000; warmup = 28000; thin = 1;
##          total post-warmup draws = 4000
## 
## Group-Level Effects: 
## ~Paper_ID (Number of levels: 108) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     1.29      0.12     1.07     1.55 1.00     2301     3012
## 
## ~season (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.89      0.46     0.38     2.12 1.00     3490     3017
## 
## ~Site_ID (Number of levels: 199) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.61      0.06     0.51     0.73 1.00     1449     2696
## 
## ~Transect_Type (Number of levels: 5) 
##               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)     0.73      0.43     0.24     1.92 1.00     2839     2610
## 
## Population-Level Effects: 
##                            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                     -2.35      0.61    -3.49    -0.97 1.00     3401
## phi_Intercept                  2.89      0.02     2.86     2.92 1.00     5308
## zi_Intercept                  -1.90      0.03    -1.95    -1.84 1.00     5870
## sYear                          0.25      0.03     0.18     0.31 1.00     7706
## sSumTemp                       0.31      0.04     0.24     0.39 1.00     7382
## OceanIndianOcean              -0.43      0.38    -1.18     0.30 1.00     3088
## OceanPacificOcean             -0.39      0.31    -1.01     0.21 1.00     2894
## sWSSTA                         0.19      0.02     0.16     0.23 1.00     7822
## sDisease_Num                   0.37      0.11     0.15     0.60 1.00     3616
## sSumTemp:OceanIndianOcean      0.04      0.11    -0.17     0.26 1.00     9325
## sSumTemp:OceanPacificOcean    -0.23      0.10    -0.42    -0.05 1.00     7484
## phi_sSumTemp                  -0.25      0.01    -0.27    -0.22 1.00     5706
## phi_sWSSTA                    -0.24      0.01    -0.26    -0.22 1.00     6155
## phi_sYear                      0.31      0.02     0.28     0.34 1.00     6297
## zi_sSumTemp                    0.13      0.03     0.08     0.19 1.00     6273
## zi_sWSSTA                     -0.17      0.03    -0.24    -0.11 1.00     6072
## zi_sYear                      -0.03      0.03    -0.08     0.02 1.00     6194
##                            Tail_ESS
## Intercept                      3100
## phi_Intercept                  2398
## zi_Intercept                   2608
## sYear                          3369
## sSumTemp                       3162
## OceanIndianOcean               2961
## OceanPacificOcean              2991
## sWSSTA                         3141
## sDisease_Num                   2997
## sSumTemp:OceanIndianOcean      3373
## sSumTemp:OceanPacificOcean     2957
## phi_sSumTemp                   2921
## phi_sWSSTA                     2854
## phi_sYear                      3504
## zi_sSumTemp                    2923
## zi_sWSSTA                      3075
## zi_sYear                       2680
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Model Comparisons

Leave-One-Out Comparisons

# Fit Models for LOO Comparison 
fit_no_interaction <- add_criterion(no_interaction, "loo")
fit_all_interaction <- add_criterion(all_interaction, "loo")
fit_YearxOcean_SSTxOcean <- add_criterion(YearxOcean_SSTxOcean, "loo")
fit_SSTxOcean_WSSTAxOcean <- add_criterion(SSTxOcean_WSSTAxOcean, "loo")
fit_YearxOcean_WSSTAxOcean <- add_criterion(YearxOcean_WSSTAxOcean, "loo")
fit_YearxOcean <- add_criterion(YearxOcean, "loo")
fit_WSSTAxOcean <- add_criterion(WSSTAxOcean, "loo")
fit_SSTxOcean <- add_criterion(SSTxOcean, "loo")


# LOO Comparison
lootest <- loo_compare(fit_SSTxOcean, 
                       fit_all_interaction, 
                       fit_no_interaction, 
                       fit_YearxOcean_SSTxOcean, 
                       fit_SSTxOcean_WSSTAxOcean, 
                       fit_YearxOcean_WSSTAxOcean, 
                       fit_YearxOcean, 
                       fit_WSSTAxOcean, 
                       criterion = "loo", 
                       model_names = NULL)

print(lootest, simplify = F)
##                            elpd_diff se_diff  elpd_loo se_elpd_loo p_loo   
## fit_no_interaction              0.0       0.0  17702.5   1243.9      1302.2
## fit_WSSTAxOcean                -6.1       9.2  17696.4   1243.9      1314.9
## fit_YearxOcean                -10.0      12.9  17692.5   1244.6      1326.0
## fit_SSTxOcean_WSSTAxOcean     -14.9      12.0  17687.7   1245.4      1326.3
## fit_YearxOcean_SSTxOcean      -17.9      17.9  17684.6   1244.8      1330.9
## fit_SSTxOcean                 -21.5      10.9  17681.0   1246.0      1340.4
## fit_all_interaction           -25.9      16.3  17676.7   1245.4      1349.9
## fit_YearxOcean_WSSTAxOcean    -29.6      14.5  17673.0   1246.0      1355.5
##                            se_p_loo looic    se_looic
## fit_no_interaction             99.7 -35405.0   2487.9
## fit_WSSTAxOcean               100.2 -35392.8   2487.9
## fit_YearxOcean                102.5 -35385.0   2489.3
## fit_SSTxOcean_WSSTAxOcean     100.5 -35375.3   2490.8
## fit_YearxOcean_SSTxOcean      100.9 -35369.2   2489.5
## fit_SSTxOcean                 103.9 -35362.0   2491.9
## fit_all_interaction           101.5 -35353.3   2490.8
## fit_YearxOcean_WSSTAxOcean    105.6 -35345.9   2492.0

WAIC Comparisons

# WAIC Comparison 
waic(no_interaction)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17666.3 1250.4
## p_waic      1338.4  119.4
## waic      -35332.7 2500.8
## 
## 443 (48.3%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(all_interaction)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17637.9 1251.7
## p_waic      1388.7  121.4
## waic      -35275.7 2503.3
## 
## 442 (48.1%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(YearxOcean_SSTxOcean)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17652.2 1251.3
## p_waic      1363.3  119.5
## waic      -35304.4 2502.5
## 
## 436 (47.5%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(SSTxOcean_WSSTAxOcean)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17649.5 1251.5
## p_waic      1364.5  119.7
## waic      -35299.0 2503.0
## 
## 445 (48.5%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(YearxOcean_WSSTAxOcean)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17651.4 1251.5
## p_waic      1377.1  121.6
## waic      -35302.8 2502.9
## 
## 455 (49.6%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(YearxOcean)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17666.7 1250.8
## p_waic      1351.9  119.0
## waic      -35333.3 2501.5
## 
## 444 (48.4%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(WSSTAxOcean)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17663.5 1249.9
## p_waic      1347.8  118.4
## waic      -35326.9 2499.9
## 
## 439 (47.8%) p_waic estimates greater than 0.4. We recommend trying loo instead.
waic(SSTxOcean)
## 
## Computed from 4000 by 918 log-likelihood matrix
## 
##           Estimate     SE
## elpd_waic  17669.2 1249.9
## p_waic      1352.2  119.3
## waic      -35338.4 2499.8
## 
## 446 (48.6%) p_waic estimates greater than 0.4. We recommend trying loo instead.
# p_waic estimates greater than 0.4, use LOO instead for comparison

We find the no_interaction and YearxOcean_SSTxOcean interaction models to be the best fits

Unscale Data

We need means and standard deviation for WSSTA, SumTemp, and Year to convert out of scale

meanSD <- rdsdat %>% summarise(Year_mean = mean(Year,na.rm = T),
                               Year_SD  = sd(Year,na.rm = T),
                               WSSTA_mean  = mean(WSSTA,na.rm = T),
                               WSSTA_SD  = sd(WSSTA,na.rm = T),
                               SumTemp_mean  = mean(average_SST_summer,na.rm = T),
                               SumTemp_SD = sd(average_SST_summer,na.rm = T)
)

meanSD
##   Year_mean  Year_SD WSSTA_mean WSSTA_SD SumTemp_mean SumTemp_SD
## 1   2006.41 5.322735   2.082593 3.757537     28.64199    1.02596

Future Predictions of Disease Prevalence

2018

# last year of data
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2018-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   2.18 0.0992    0.0208     0.245
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

2022

no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2022-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   2.93  0.117    0.0267     0.282
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

2050

no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2050-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   8.19  0.319     0.102     0.598
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

2100

no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2100-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   17.6  0.768     0.532     0.929
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

2100 with RCP 8.5

# 2015 IPCC RCP 8.5 "business as usual" predictions
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2100-meanSD$Year_mean)/meanSD$Year_SD, sSumTemp = (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   17.6  0.805     0.644     0.924
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

Average summer SST alone increasing to RCP 8.5 levels

# effect of if the Year didn't accelerate past last data point, and only average summer temperature changed
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2018-meanSD$Year_mean)/meanSD$Year_SD, sSumTemp = (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   2.18  0.196    0.0547     0.411
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

WSSTA Doubles

# Effect of WSSTA independent of year and summer temperature
# assume arbitrarily that anomalies will get 2x more intense in future
# keeping year at 2018 so we don't account for any predicted changes in temperature that would occur when we predict the future years
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2018-meanSD$Year_mean)/meanSD$Year_SD, sWSSTA = ((meanSD$WSSTA_mean*2)-meanSD$WSSTA_mean)/meanSD$WSSTA_SD),
          epred = TRUE,
          re_formula = NA)
##  sYear emmean lower.HPD upper.HPD
##   2.18  0.111    0.0231     0.267
## 
## Point estimate displayed: median 
## HPD interval probability: 0.95

Figures

Figure 1

Location data characteristics

Figure 1A

earth <- map_data("world") # World map

# Map of survey locations, colored by Ocean basin
OceanMap <- ggplot() +
  geom_map(data = earth, 
           map = earth, 
           aes(x = long, y = lat, group = group, map_id = region), 
           fill = "grey95", 
           colour = "grey65", 
           size = 0.2) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_manual(breaks = c("TRUE", "FALSE"), values = c("darkorchid3", "gold2")) +
  scale_x_continuous(breaks = c(-180,-120,-60,0,60,120,180)) +
  scale_y_continuous(breaks = c(-66,-23,0,23,66), limits = c(-77,77)) +
  geom_point(data = rdsdat, aes(x = Lon, y = Lat, fill = Ocean,
                       colour = as.factor(rdsdat$Lat > 0)),
             alpha = 0.4, colour = "grey50",
             size = 4, shape = 21) +
  guides(fill = "legend") +
  #coord_quickmap() +
  labs(x = "Longitude", y = "Latitude") +
  theme_bw(base_size = 20) +
  theme(panel.grid.minor = element_blank()) +
  theme (legend.position = "none")

# Histogram of surveys by latitude
OceanMapM <- ggMarginal(OceanMap, type = "hist", margins = "y", size = 10,
                        bins = 31, fill = "grey40",
                        colour = "white")

Figure 1B

# Subset Northern Hemisphere data
poslat <- subset(rdsdat, Lat > "0") 

# Table for Northern Hemisphere values
table(poslat$start_month) -> Ncounts
# Change table to dataframe
Ncounts <- as.data.frame(t(Ncounts))
# Rename values to actual month name for axis values
Ncounts$Var2 <- c('Jan', 
                  "Feb", 
                  'Mar', 
                  'Apr', 
                  'May', 
                  'Jun', 
                  'Jul', 
                  'Aug', 
                  'Sep', 
                  'Oct', 
                  'Nov', 
                  'Dec')
# Rename column for axis label
names(Ncounts)[2] <- 'Month'


# Subset Southern Hemisphere data
neglat <- subset(rdsdat, Lat < "0")

# Table for Southern Hemisphere values
Scounts <- table(neglat$start_month)
# Change table to dataframe
Scounts <- as.data.frame(t(Scounts))
# Rename values to actual month name for axis values
Scounts$Var2 <- c('Jan', 
                  "Feb", 
                  'Mar', 
                  'Apr', 
                  'May', 
                  'Jun', 
                  'Jul', 
                  'Aug', 
                  'Sep', 
                  'Oct', 
                  'Nov', 
                  'Dec')
# Rename column for axis label
names(Scounts)[2] <- 'Month'


# Plot number of estimates for each month per Hemisphere
Hemi <- ggplot(Scounts) +
  geom_bar(aes(x=Month, y=-1*Freq), 
           color = "gold2", 
           fill = "gold2", 
           stat = 'identity') +
  theme_bw(base_size = 20) +
  theme(panel.grid.minor = element_blank()) +
  geom_bar(data = Ncounts, aes(x=Month, y=Freq),
           stat = 'identity',
           color = "darkorchid3",
           fill = "darkorchid3") +
  scale_y_continuous(breaks = c(-50,0,50,100,150,200),
                     labels = c(50,0,50,100,150,200)) +
  scale_x_discrete(limits = c('Jan', 
                              "Feb", 
                              'Mar', 
                              'Apr', 
                              'May', 
                              'Jun', 
                              'Jul', 
                              'Aug', 
                              'Sep', 
                              'Oct', 
                              'Nov', 
                              'Dec'),
                   labels = c('J','F','M','A','M','J','J','A','S','O','N','D'),
                   position = "bottom") +
  labs(x = "", y = "No. of estimates in N/S Hemisphere")

Combine plots

ggarrange(OceanMapM, Hemi, widths = c(3,1), labels = c("A","B"))

## {-}

Figure 2

Changes in disease prevalence over the three factors: average summer sea surface temperature (SST) in °C, weekly sea surface temperature anomaly (WSSTA) in °C-weeks, and Year

# Colour palette
palette()

# getting data from the model
dat <- no_interaction$data

Figure 2A

# summer temp

# plotting lines
means_fixed <- no_interaction %>% 
  emmeans(~ sSumTemp,
          at = list(sSumTemp = seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% as_tibble() %>% 
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

#adding rSumTemp to dat
dat <- dat %>% 
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

# putting bubbles in plot
temp1 <- ggplot()+
  # confidence interval
  geom_smooth(data = means_fixed, aes(x = rSumTemp, y = lower.HPD), method =  "loess", formula = y~x, se = FALSE, lty =  "dotted", lwd = 0.5,  col = "black") +
  geom_smooth(data = means_fixed, aes(x = rSumTemp, y = upper.HPD), method =  "loess", formula = y~x, se = FALSE, lty = "dotted", lwd = 0.5,  col = "black") +
  # main line
  geom_smooth(data = means_fixed, aes(x = rSumTemp, y = emmean), method =  "loess", formula = y~x, se = FALSE, lwd = 1, col = "black") +
  geom_point(data = dat, aes(x = rSumTemp, y = Disease_P, size = logArea, fill = Ocean), shape = 21, alpha = 0.5) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_viridis_d(end = 0.9) +
  #facet_grid(rows = vars(condition)) +
  labs(y = "Proportion of disease prevalence", x = "Average Summer SST (\u00B0C)", size = expression(paste("log(Area Examined [", cm^2, "])", sep = "")), fill = "Ocean") +
  ylim(0, 1.0) + xlim(24.5, 32.5) +
  # themes
  theme_bw(base_size = 12) +  theme(legend.position="bottom", legend.box="vertical", legend.margin=margin())

Figure 2B

# plotting gradients
means_draws <- no_interaction %>% 
  emmeans(~ sSumTemp,
          at = list(sSumTemp = seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% 
  gather_emmeans_draws() %>%
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

temp2 <- ggplot(means_draws, aes(x = rSumTemp, y = .value)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greys") +
  labs(x = "Average Summer SST (\u00B0C)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  ylim(0, 0.25) + xlim(24.5, 32.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

Figure 2C

# comparing min and max point  
trends_draws <- no_interaction %>% 
  emtrends(~ sSumTemp, var = "sSumTemp",
           at = list(sSumTemp = c((25-meanSD$SumTemp_mean)/meanSD$SumTemp_SD, (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD)), # 25 and 32 degree
           regrid = "response") %>% 
  gather_emmeans_draws() %>%
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

# plot
temp3 <- ggplot(trends_draws, aes(x = .value, fill = factor(rSumTemp))) +
  geom_vline(xintercept = 0) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               slab_alpha = 0.75) +
  scale_fill_viridis_d(option = "magma", begin = 0.4, end = 0.8) +
  labs(x = "Average marginal effects at the two temperatures below", 
       y = "Density", fill = "rSumTemp",
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom") +
  labs(fill = "Average Summer SST (\u00B0C)")

Figure 2D

# WSSTA

# plotting lines
means_fixed <- no_interaction %>% 
  emmeans(~ sWSSTA,
          at = list(sWSSTA = seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% as_tibble() %>% 
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))

#adding rWSSTA to dat
dat <- dat %>% 
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))


wssta1 <- ggplot()+
  # confidence interval
  geom_smooth(data = means_fixed, aes(x = rWSSTA, y = lower.HPD), method =  "loess", formula = y~x, se = FALSE, lty =  "dotted", lwd = 0.5,  col = "black") +
  geom_smooth(data = means_fixed, aes(x = rWSSTA, y = upper.HPD), method =  "loess", formula = y~x, se = FALSE, lty = "dotted", lwd = 0.5,  col = "black") +
  # main line
  geom_smooth(data = means_fixed, aes(x = rWSSTA, y = emmean), method =  "loess", formula = y~x, se = FALSE, lwd = 1, col = "black") +
  geom_point(data = dat, aes(x = rWSSTA, y = Disease_P, size = logArea, fill = Ocean), shape = 21, alpha = 0.5) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_viridis_d(end = 0.9) +
  labs(y = "Proportion of disease prevalence", x = "WSSTA (\u00B0C-weeks)",
       fill = "Ocean",
       size = expression(paste("log(Area  Examined[", cm^2, "])", sep = ""))) +
  ylim(0, 1.0) + xlim(0, 3.5) +
  theme_bw(base_size = 12) +  theme(legend.position="bottom", legend.box="vertical", legend.margin=margin()) +
  guides(size = guide_legend(order = 1), fill = guide_legend(order = 2))

Figure 2E

# plotting gradients
means_draws <- no_interaction %>% 
  emmeans(~ sWSSTA,
          at = list(sWSSTA = seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% 
  gather_emmeans_draws() %>%
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))



wssta2 <- ggplot(means_draws, aes(x = rWSSTA, y = .value)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greys") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  ylim(0, 0.25) + xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

Figure 2F

# comparing min and max point  
trends_draws <- no_interaction %>% 
  emtrends(~ sWSSTA, var = "sWSSTA",
           at = list(sWSSTA = c((0.4-meanSD$WSSTA_mean)/meanSD$WSSTA_SD, (4.3-meanSD$WSSTA_mean)/meanSD$WSSTA_SD)), 
           regrid = "response") %>% 
  gather_emmeans_draws() %>%
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))


wssta3 <- ggplot(trends_draws, aes(x = .value, fill = factor(rWSSTA))) +
  geom_vline(xintercept = 0) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               slab_alpha = 0.75) +
  scale_fill_viridis_d(option = "magma", begin = 0.4, end = 0.8) +
  labs(x = "Average marginal effect at the two WSSTA values below", 
       y = "Density", fill = "rWSSTA",
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom") +
  labs(fill = "WSSTA (\u00B0C-weeks)")

Figure 2G

# year

# plotting lines
means_fixed <- no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = seq(min(dat$sYear),max(dat$sYear),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% as_tibble() %>% 
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

#adding rYear to dat
dat <- dat %>% 
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

# putting bubbles
year1 <- ggplot()+
  # confidence interval
  geom_smooth(data = means_fixed, aes(x = rYear, y = lower.HPD), method =  "loess", formula = y~x, se = FALSE, lty =  "dotted", lwd = 0.5,  col = "black") +
  geom_smooth(data = means_fixed, aes(x = rYear, y = upper.HPD), method =  "loess", formula = y~x, se = FALSE, lty = "dotted", lwd = 0.5,  col = "black") +
  # main line
  geom_smooth(data = means_fixed, aes(x = rYear, y = emmean), method =  "loess", formula = y~x, se = FALSE, lwd = 1, col = "black") +
  geom_point(data = dat, aes(x = rYear, y = Disease_P, size = logArea, fill = Ocean), shape = 21, alpha = 0.5) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_viridis_d(end = 0.9) + 
  #facet_grid(rows = vars(condition)) +
  labs(y = "Proportion of disease prevalence", x = "Year", 
       fill = "Ocean", 
       size = expression(paste("log(Area Examined [", cm^2, "])", sep = ""))) +
  ylim(0, 1.0) + xlim(1990, 2020) +
  # themes
  theme_bw(base_size = 12) +  theme(legend.position="bottom", legend.box="vertical", legend.margin=margin())

Figure 2H

# plotting gradients
means_draws <- no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = seq(min(dat$sYear),max(dat$sYear),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% 
  gather_emmeans_draws() %>%
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

year2 <- ggplot(means_draws, aes(x = rYear, y = .value)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greys") +
  labs(x =  "Year", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  ylim(0, 0.25) + xlim(1990, 2020) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

Figure 2I

# comparing min and max point  
trends_draws <- no_interaction %>% 
  emtrends(~ sYear, var = "sYear",
           at = list(sYear = c((1992-meanSD$Year_mean)/meanSD$Year_SD, (2018-meanSD$Year_mean)/meanSD$Year_SD)),
           regrid = "response") %>% 
  gather_emmeans_draws() %>%
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

year3 <- ggplot(trends_draws, aes(x = .value, fill = factor(rYear)), ) +
  geom_vline(xintercept = 0) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               slab_alpha = 0.75) +
  scale_fill_viridis_d(option = "magma", begin = 0.4, end = 0.8) +
  labs(x = "Average marginal effects at the two years below", 
       y = "Density", fill = "rYear",
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom") +
  labs(fill = "Year")

Combine plots

(temp1 + temp2 + temp3) / 
  (wssta1  + wssta2  + wssta3)   / 
  (year1 + year2 + year3) + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))

## {-}

Figure 3

Global disease prevalence prediction depicted three ways

Figure 3A

# summer temp

# new data range
sSumTemp_seq <- seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)

# creating new data
new_st<- expand_grid(
  sYear = 0,
  sSumTemp = sSumTemp_seq,
  sWSSTA = 0,
  sDisease_Num = 0,
  Ocean = c("Atlantic Ocean","Pacific Ocean","Indian Ocean") # this is for Atlantic
)


res_draws <- no_interaction %>% 
  epred_draws(newdata = new_st, dpar = c("mu", "zi", "phi"), re_formula = NA) %>% 
  group_by(sSumTemp, .draw) %>% summarise(sSumTemp = mean(sSumTemp),
                                          rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean),
                                          predicted = mean(.epred),
                                          mu = mean(mu),
                                          zi = mean(zi),
                                          phi = mean(phi)) 

# mu
temp_mu <- ggplot(res_draws, aes(x = rSumTemp, y = mu)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Reds") +
  labs(x =  "Average Summer SST (\u00B0C)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")

Figure 3B

# zi
temp_zi <- ggplot(res_draws, aes(x = rSumTemp, y = zi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Blues") +
  labs(x =  "Average Summer SST (\u00B0C)", y = "Proportion of 0 disease prevalence",
       fill = "Credible interval") +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")

Figure 3C

# phi
temp_phi <- ggplot(res_draws, aes(x = rSumTemp, y = phi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greens") +
  labs(x =  "Average Summer SST (\u00B0C)", y = "Precison (1/SE) of disease prevalence",
       fill = "Credible interval") +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")

Figure 3D

# WSSTA

# new data range
sWSSTA_seq <- seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)

# creating new data
new_wssta<- expand_grid(
  sYear = 0,
  sSumTemp = 0,
  sWSSTA = sWSSTA_seq,
  sDisease_Num = 0,
  Ocean = c("Atlantic Ocean","Pacific Ocean","Indian Ocean") # this is for Atlantic
)


res_draws <- no_interaction %>% 
  epred_draws(newdata = new_wssta, dpar = c("mu", "zi", "phi"), re_formula = NA) %>% 
  group_by(sWSSTA, .draw) %>% summarise(sWSSTA = mean(sWSSTA),
                                        rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean),
                                        predicted = mean(.epred),
                                        mu = mean(mu),
                                        zi = mean(zi),
                                        phi = mean(phi))

# mu
wssta_mu <- ggplot(res_draws, aes(x = rWSSTA, y = mu)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Reds") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  #ylim(0, 0.30) + 
  xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
#theme(legend.position = "bottom")

Figure 3E

# zi
wssta_zi <- ggplot(res_draws, aes(x = rWSSTA, y = zi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Blues") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Proportion of 0 disease prevalence",
       fill = "Credible interval") +
  #ylim(0, 0.30) + 
  xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
#theme(legend.position = "bottom")

Figure 3F

# phi
wssta_phi <- ggplot(res_draws, aes(x = rWSSTA, y = phi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greens") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Precison (1/SE) of disease prevalence",
       fill = "Credible interval") +
  xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")

Figure 3G

# year

# new data range
sYear_seq <- seq(min(dat$sYear),max(dat$sYear),length.out = 100)

# creating new data
new_year<- expand_grid(
  sYear = sYear_seq,
  sSumTemp = 0,
  sWSSTA = 0,
  sDisease_Num = 0,
  Ocean = c("Atlantic Ocean","Pacific Ocean","Indian Ocean") # this is for Atlantic
)

res_draws <- no_interaction %>% 
  epred_draws(newdata = new_year, dpar = c("mu", "zi", "phi"), re_formula = NA) %>% 
  group_by(sYear, .draw) %>% summarise(sYear = mean(sYear),
                                       rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean),
                                       predicted = mean(.epred),
                                       mu = mean(mu),
                                       zi = mean(zi),
                                       phi = mean(phi)) 

# mu
year_mu <- ggplot(res_draws, aes(x = rYear, y = mu)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Reds") +
  labs(x =  "Year", y = "Proportion of disease prevalence",
       fill = "Credible interval for mu") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

Figure 3H

# zi
year_zi <- ggplot(res_draws, aes(x = rYear, y = zi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Blues") +
  labs(x =  "Year", y = "Proportion of 0 disease prevalence",
       fill = "Credible interval for zi") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

Figure 3I

# phi
year_phi <- ggplot(res_draws, aes(x = rYear, y = phi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greens") +
  labs(x =  "Year", y = "Precison (1/SE) of disease prevalence",
       fill = "Credible interval for phi") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

Combine plots

(temp_mu + temp_zi + temp_phi)   / 
  (wssta_mu  + wssta_zi  + wssta_phi)   / 
  (year_mu + year_zi + year_phi)  + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))

## {-}

Figure 4

Three oceans’ predicted non-zero values (mu) of disease prevalence per fixed variable

Getting data ready

dat <-YearxOcean_SSTxOcean$data

Figure 4A

# summer temp

# create new data
means_fixed <- YearxOcean_SSTxOcean %>% 
  emmeans(~ sSumTemp + Ocean,
          at = list(sSumTemp = seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>%  
  gather_emmeans_draws() %>%
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

# separate data by ocean and plot
means_fixed_A <- subset(means_fixed, Ocean == "Atlantic Ocean")
means_fixed_I <- subset(means_fixed, Ocean == "Indian Ocean")
means_fixed_P <- subset(means_fixed, Ocean == "Pacific Ocean")

# Atlantic Ocean
temp_A <- ggplot(means_fixed_A, aes(x = rSumTemp, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  #facet_wrap(vars(Ocean), ncol = 3) +
  labs(x = "Average Summer SST (\u00B0C)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "none", text = element_text(size = 12))

Figure 4B

# Indian Ocean
temp_I <- ggplot(means_fixed_I, aes(x = rSumTemp, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.45) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Average Summer SST (\u00B0C)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "none", text = element_text(size = 12))

Figure 4C

# Pacific Ocean
temp_P <- ggplot(means_fixed_P, aes(x = rSumTemp, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.9) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Average Summer SST (\u00B0C)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "none", text = element_text(size = 12))

Figure 4D

#wssta

# create new data
means_fixed <- YearxOcean_SSTxOcean %>% 
  emmeans(~ sWSSTA + Ocean,
          at = list(sWSSTA = seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>%  
  gather_emmeans_draws() %>%
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))

# separate data by ocean and plot
means_fixed_A <- subset(means_fixed, Ocean == "Atlantic Ocean")
means_fixed_I <- subset(means_fixed, Ocean == "Indian Ocean")
means_fixed_P <- subset(means_fixed, Ocean == "Pacific Ocean")

# Atlantic Ocean
wssta_A <- ggplot(means_fixed_A, aes(x = rWSSTA, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "WSSTA (\u00B0C-weeks)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.2) +
  xlim(0,3.5) +
  theme(legend.position = "none", text = element_text(size = 12))

Figure 4E

# Indian Ocean
wssta_I <- ggplot(means_fixed_I, aes(x = rWSSTA, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.45) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "WSSTA (\u00B0C-weeks)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.2) +
  xlim(0,3.5) +
  theme(legend.position = "none", text = element_text(size = 12))

Figure 4F

# Pacific Ocean
wssta_P <- ggplot(means_fixed_P, aes(x = rWSSTA, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.9) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "WSSTA (\u00B0C-weeks)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.2) +
  xlim(0,3.5) +
  theme(legend.position = "none", text = element_text(size = 12))

Figure 4G

# year

# create new data
means_fixed <- YearxOcean_SSTxOcean %>% 
  emmeans(~ sYear + Ocean,
          at = list(sYear = seq(min(dat$sYear),max(dat$sYear),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>%  
  gather_emmeans_draws() %>%
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

# separate data by ocean and plot
means_fixed_A <- subset(means_fixed, Ocean == "Atlantic Ocean")
means_fixed_I <- subset(means_fixed, Ocean == "Indian Ocean")
means_fixed_P <- subset(means_fixed, Ocean == "Pacific Ocean")

# Atlantic Ocean
year_A <- ggplot(means_fixed_A,
                 aes(x = rYear, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Year",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "bottom", text = element_text(size = 12))

Figure 4H

# Indian Ocean
year_I <- ggplot(means_fixed_I,
                 aes(x = rYear, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.45) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Year",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "bottom", text = element_text(size = 12))

Figure 4I

# Pacific Ocean
year_P <- ggplot(means_fixed_P,
                 aes(x = rYear, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.9) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Year",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "bottom", text = element_text(size = 12))

Combine plots

(temp_A + temp_I + temp_P) / 
  (wssta_A  + wssta_I  + wssta_P) / 
  (year_A + year_I + year_P) + plot_annotation(tag_levels = 'A') + 
  plot_layout(guides = "collect") & 
  theme(legend.position='bottom', 
        text = element_text(size = 12))

## {-}

Figure S4

Correlation between Average Summer SST and Year

ggplot(rdsdat) +
  geom_point(aes(y = average_SST_summer, x = Year)) +
  labs(y = "Average Summer SST (\u00B0C)") +
  theme_bw()

Figure S5

Visual description of WSSTA calculation

# define an updated comparison operator that will work well with values differing beyond the floating point precision limit

`%===%` <- function(x, y, tol = 1e-7) {
  
  if(length(x) == 1) {
    a = x; b = y
  } else {
    a = y; b = x
  }
  
  if(length(a) == 1 & length(b) == 1) {
    testout <- isTRUE(all.equal(x, y, tolerance = tol))
  }
  
  if(length(a) == 1 | length(b) == 1) {
    testout <- sapply(b, function(n, num) isTRUE(all.equal(n, num, tolerance = tol)), num = a)
  }
  
  if(length(a) > 1 & length(b) > 1) {
    testout <- mapply(function(n, m) isTRUE(all.equal(n, m, tolerance = tol)), a, b)
    
    if(length(a) != length(b)) warning("Objects differ in length, recycling the shorter object!")
  }
  
  return(testout)
}

# tests of the function
1 %===% 1
c(1,2,3) %===% 2
c(1,2,3) %===% c(3,2,4)
2 %===% c(3,2,4)
2.0005 %===% c(3,2,4)
2.00000006 %===% c(3,2.00000003,4)
2 %===% c(3,2.00000003,4)
c(1,2,3,4) %===% c(1,3)

# open a connection to a sample file to see its attributes
# (commented as it's usable only with all individual files available)
# nc_temp <- nc_open('./2015/20150101120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_CDR2.1-v02.0-fv01.0.nc')
# print(nc_temp)

# example of extracting only some of a specific variable
# varpart <- ncvar_get(nc_temp, "analysed_sst", start = c(1,50,1), count = c(-1,10,1))
# is equivalent to this
# varpart <- ncvar_get(nc_temp, "analysed_sst", start = c(1,50), count = c(-1,10))
# dimensions are provided as X-Y-(Z)-T, and they are lon-lat-time

# extract the allowed values of dimensions
# (commented for reasons explained above)
# lonvar <- ncvar_get(nc_temp, "lon")
# latvar <- ncvar_get(nc_temp, "lat")

# reload limited data (defined largely in several commented lines)
# note: full NC file data are not included in repo due to their size
load(here('R', 'dhw_illustration_plot', 'required.Rdata'))

# load climatology
mmm <- nc_open(here("data",'ct5km_climatology_v3.1.nc'))
lons <- round(ncvar_get(mmm, 'lon'), 3)
lats <- round(ncvar_get(mmm, 'lat'), 3)

# check coordinates
lon_index <- which(lonvar %===% round(floor(114.6548/0.05)*0.05 + 0.025, 3))
lat_index <- which(latvar %===% round(floor(-8.14028/0.05)*0.05 + 0.025, 3))
mmmlon <- which(lons %===% round(floor(lonvar[lon_index]/0.05)*0.05 + 0.025, 3))
mmmlat <- which(lats %===% round(floor(latvar[lat_index]/0.05)*0.05 + 0.025, 3))

# (commented for reasons explained above)
# sst_ts <- c()
# for(i in list.files("./2017/")) {
#   nc_temp <- nc_open(paste0("./2017/", i))
#   sst_data <- ncvar_get(nc_temp, "analysed_sst",
#                         start = c(lon_index, lat_index, 1), count = c(1,1,1))
#   sst_ts <- c(sst_ts, sst_data)
#   nc_close(nc_temp)
# }
# repeat above code to load separate sst data for 2015, 2016, 2017
# sst_ts -> sst_ts_2015
# sst_ts -> sst_ts_2016
# sst_ts -> sst_ts_2017

# below code saves .Rdata file that is later used to reload yearly data
# limited to specific coordinates
# save(list = c('sst_ts_2015', 'sst_ts_2016', 'sst_ts_2017', 'lonvar', 'latvar'),
#      file = here('R', 'dhw_illustration_plot', 'required.Rdata'))


# extract location's climatology
mmm_climatology <- c(
  ncvar_get(mmm, "sst_clim_january", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_february", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_march", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_april", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_may", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_june", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_july", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_august", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_september", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_october", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_november", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_december", start = c(mmmlon, mmmlat, 1), count = c(1,1,1))
)
mmm_climatology

sst_ts <- sst_ts_2017 # change this to change year (data are: sst_ts_2015/2016/2017)
data <- data.frame(day = 1:length(sst_ts), sst = sst_ts-273)

# calculate month middles (floor-rounded)
# define months
months <- c(31,29,31,30,31,30,31,31,30,31,30,31)
months_cumul <- c(1, cumsum(months))
m_mids <- c()
for (i in 1:12) {
  mid <- floor((months_cumul[i] + months_cumul[i+1])/2)
  m_mids <- c(m_mids, mid)
}

weeks <- c(1, 1:52 * 7)
w_mids <- c()
for (i in 1:length(weeks)) {
  mid <- floor((weeks[i] + weeks[i+1])/2)
  if(!is.na(mid)) w_mids <- c(w_mids, mid)
}

i <- 1
sst_weekly <- c()
for(temp in data$sst) {
  
  if(i == 1) sst_avg <- temp*1/7
  else sst_avg <- sst_avg + temp*1/7
  
  if(i == 7) {sst_weekly <- c(sst_weekly, sst_avg); i <- 1}
  else i <- i+1
}

data.w <- data.frame(w_mids = w_mids, w_means = sst_weekly,
                     wssta = cumsum(ifelse(sst_weekly > max(mmm_climatology)+1,
                                         sst_weekly-max(mmm_climatology)+1, 0)))

# scale and fact are used to transform the second y axis
scale <- 25
fact <- 25
plot <- ggplot(data = data, mapping = aes(x = day)) +
  geom_hline(yintercept = max(mmm_climatology), lty = 5, lwd = 0.5, col = 'purple') +
  geom_hline(yintercept = max(mmm_climatology) + 1, lty = 5, lwd = 0.5, col = 'red') +
  scale_x_continuous(breaks = months_cumul) +
  scale_y_continuous(name = 'Sea Surface Temperature (\u00B0C)', limits = c(25, 31),
                     sec.axis = sec_axis(~(.-fact)*scale, name = "Cumulative Heat Stress (\u00B0C-weeks)")) +
  geom_line(aes(y = sst), col = 'gray80', lwd = 1.5) +
  geom_segment(data = subset(data.w, w_means > max(mmm_climatology) + 1),
               aes(x = w_mids, y = max(mmm_climatology) + 1, xend = w_mids, yend = w_means),
               lwd = 2, col = 'coral') +
  geom_point(data = data.frame(m_mids = m_mids, m_means = mmm_climatology),
             aes(x = m_mids, y = m_means), shape = 3, size = 5, col = 'red', stroke = 1) +
  geom_point(data = data.w,
             aes(x = w_mids, y = w_means), shape = 19, size = 2.5, col = 'gray40') +
  geom_ribbon(data = data.w, aes(x = w_mids, ymax = (wssta/scale)+fact), ymin = fact, fill = 'red', alpha = 0.1) +
  geom_line(data = data.w, aes(x = w_mids, y = (wssta/scale)+fact), col = 'coral', lwd = 3) +
  geom_point(x = max(data.w$w_mids), y = max((data.w$wssta/scale)+fact), shape = 19, size = 6, col = 'coral') +
  theme_bw() +
  theme(panel.grid.minor.x = element_blank(), text = element_text(size = 20)) +
  labs(x = 'Days')
plot

Figure S7

Phylogenetic Tree of included species

genus <- unique(prevSPP$Genus[prevSPP$Genus != "Helioseris"]) # Removed the Genus Helioseris because it was removed from the analysed dataset because of the disease prevalence metric used
taxa_list <- na.omit(genus)
taxa_list2 <- as.factor(taxa_list)
taxa <- tnrs_match_names(names = levels(taxa_list2), context_name = "Animals") # Connect the list of Genera with the Open Tree taxonomy

taxa.in.tree <- ott_id(taxa)[is_in_tree(ott_id(taxa))] 

tree <- tol_induced_subtree(ott_ids = taxa.in.tree, label_format = "name")
tree$tip.label <- gsub("_\\(.*","", tree$tip.label) # Remove symbols

plain_tree <- plot(tree, cex = 0.5, label.offset = 0.1, no.margin = TRUE) 

is.binary(tree) # Check if binary
## [1] FALSE
tree <- compute.brlen(tree, method = "Grafen", power = 1) # Compute branch lengths 

all_taxa <- intersect(as.character(tree$tip.label), taxa_list) # Find synonyms and typos and remove them
used_taxa <- setdiff(as.character(tree$tip.label), taxa_list)

# Build heatmap of oceans
species <- select(prevSPP, "PaperID", "Genus")
species$Paper_ID <- species$PaperID
dat.tree <- left_join(rdsdat, species, by = c("Paper_ID")) # Join the data with the Genus information
dat.tree <- select(dat.tree, Paper_ID, Genus, Ocean) # Reduce the data to the needed columns
dat.tree <- mutate(dat.tree, search_string = decapitalize(Genus)) # Decapitalize "Genus" to match the "search string" in the "taxa" file
dat.tree <- left_join(dat.tree, select(taxa, search_string, unique_name, ott_id), by = "search_string") # Join taxa information from rotl to the dataset

dat.tree$unique_name <- word(dat.tree$unique_name, 1) # Only keep one word for the Genus (we had e.g. "Stylophora (genus in phylum Cnidaria)")

dat.tree <- dat.tree[dat.tree$unique_name %in% tree$tip.label, ] # Check that the Genera match the ones used in the tree

ocean <- dat.tree %>% group_by(unique_name) %>% summarise( 
  oceanIndian = Ocean == "Indian Ocean", 
  oceanAtlantic = Ocean == "Atlantic Ocean",
  oceanPacific = Ocean == "Pacific Ocean")
ocean <- distinct(ocean) # Only keep unique rows
ocean$oceanIndian = as.numeric(ocean$oceanIndian) # convert TRUE/FALSE to binary values for each Ocean
ocean$oceanAtlantic = as.numeric(ocean$oceanAtlantic)
ocean$oceanPacific = as.numeric(ocean$oceanPacific)

ocean <- ocean %>% group_by(unique_name) %>% summarise(oceanIndian=sum(oceanIndian), # calculate the sum for each species (i.e., if 1, the species is found in the designated ocean)
                                                     oceanAtlantic=sum(oceanAtlantic),
                                                     oceanPacific=sum(oceanPacific))
ocean$oceanIndian=as.factor(ocean$oceanIndian) # Convert back to factor for the plot
ocean$oceanAtlantic=as.factor(ocean$oceanAtlantic)
ocean$oceanPacific=as.factor(ocean$oceanPacific)

# Plot Horizontal Tree
# simple phylogenetic tree
htree <- ggtree(tree, lwd = 1.05) + 
  geom_tiplab(offset = 0.04) # Display Genus

h <- htree %<+% ocean # Link plot to data

# plot tree and heatmap together
h2 <- h +  geom_fruit(geom=geom_tile, 
                     mapping=aes(fill=oceanAtlantic), 
                     width=0.075, 
                     offset=0.35) + 
  scale_fill_manual(name = "", values=c("white", "#7C4F85"), labels=c("", "Atlantic Ocean"), guide = guide_legend(order = 1)) +
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanIndian), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white","#71A8AF"), labels=c("", "Indian Ocean"), guide = guide_legend(order = 2))+
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanPacific), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white", "#D4E974"), labels=c("", "Pacific Ocean"), guide = guide_legend(order = 3))

h2

Figure S8

No interaction contrasts

Average Summer SST

# create new data
trends_draws2 <- no_interaction %>% 
  emtrends(~ sSumTemp, var = "sSumTemp",
           at = list(sSumTemp = c((25-meanSD$SumTemp_mean)/meanSD$SumTemp_SD, (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD)), # 25 and 32 degree
           regrid = "response") %>% 
  contrast(method = "revpairwise") %>% 
  gather_emmeans_draws()

# plot contrast
temp_cont <- ggplot(trends_draws2, aes(x = .value)) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               fill = "grey65") +
  labs(x = "Difference in marginal effects between 25\u00B0C and 32\u00B0C", y = NULL) +
  theme_bw() 

WSSTA

# create new data
trends_draws2 <- no_interaction %>% 
  emtrends(~ sWSSTA, var = "sWSSTA",
           at = list(sWSSTA = c((0.4-meanSD$WSSTA_mean)/meanSD$WSSTA_SD, (4.3-meanSD$WSSTA_mean)/meanSD$WSSTA_SD)), 
           regrid = "response") %>% 
  contrast(method = "revpairwise") %>% 
  gather_emmeans_draws()

wssta_cont <- ggplot(trends_draws2, aes(x = .value)) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               fill = "grey65") +
  labs(x = "Difference in marginal effects between 0.4\u00B0C-weeks and 4.3\u00B0C-weeks", y = NULL) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = "dotted")

Year

# create new data
trends_draws2 <- no_interaction %>% 
  emtrends(~ sYear, var = "sYear",
           at = list(sYear = c((1992-meanSD$Year_mean)/meanSD$Year_SD, (2018-meanSD$Year_mean)/meanSD$Year_SD)),
           regrid = "response") %>% 
  contrast(method = "revpairwise") %>% 
  gather_emmeans_draws()

# plot contrast
year_cont <- ggplot(trends_draws2, aes(x = .value)) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               fill = "grey65") +
  labs(x = "Difference in marginal effects between 1988 and 2018", y = NULL,
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = "dotted")

Contrast figure

temp_cont + wssta_cont + year_cont + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))

## {-}

Figure S9

Ocean contrast plots

Average Summer SST

# create new data
trends_draws <- YearxOcean_SSTxOcean %>% 
  emtrends(~  Ocean, var = "sSumTemp",
           #at = list(sSumTemp = c(0)),
           regrid = "none") %>% 
  contrast(method = "pairwise") %>% 
  gather_emmeans_draws()

# plot contrast
cont_temp <- ggplot(trends_draws,
                    aes(x = .value, fill = factor(contrast))) +
  stat_halfeye(slab_alpha = 0.75) +
  scale_fill_okabe_ito(order = c(3, 4, 5)) +
  facet_wrap(vars(contrast)) +
  theme_bw() +
  labs(x = "Difference in marginal effects between 25\u00B0C and 32\u00B0C", y = NULL)  +
  theme(legend.position = "none")

WSSTA

No WSSTA since there was no interaction with Ocean

Year

# create new data
trends_draws <- YearxOcean_SSTxOcean %>% 
  emtrends(~  Ocean, var = "sYear",
           #at = list(sSumTemp = c(0)),
           regrid = "none") %>% 
  contrast(method = "pairwise") %>% 
  gather_emmeans_draws()

# plot contrast
cont_year<- ggplot(trends_draws,
                   aes(x = .value, fill = factor(contrast))) +
  stat_halfeye(slab_alpha = 0.75) +
  scale_fill_okabe_ito(order = c(3, 4, 5)) +
  facet_wrap(vars(contrast)) +
  theme_bw() +
  labs(x = "Difference in marginal effects between 1992 and 2018", y = NULL,
       caption = "80% and 95% credible intervals shown in black") +
  theme(legend.position = "bottom", legend.title=element_blank())

Contrast figure

cont_temp / cont_year + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))

## {-}

Software and Package Versions

sessionInfo()
## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22000)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.3.4     ggExtra_0.10.0       ggpubr_0.4.0        
##  [4] igraph_1.3.1         ggdist_3.1.1         ggbeeswarm_0.6.0    
##  [7] gghalves_0.1.1       ggokabeito_0.1.0     patchwork_1.1.1     
## [10] emmeans_1.7.3        broom.mixed_0.2.9.4  broom_0.8.0         
## [13] tidybayes_3.0.2      brms_2.17.0          Rcpp_1.0.8.3        
## [16] modelr_0.1.8         glmmTMB_1.1.3        rstan_2.21.5        
## [19] StanHeaders_2.21.0-7 lme4_1.1-29          Matrix_1.4-1        
## [22] birk_2.1.2           RCurl_1.98-1.6       lubridate_1.8.0     
## [25] ncdf4_1.19           R.utils_2.11.0       R.oo_1.24.0         
## [28] R.methodsS3_1.8.1    ggnewscale_0.4.7     ggtreeExtra_1.7.0   
## [31] ggtree_3.5.0.901     BiocManager_1.30.17  RDS_0.9-3           
## [34] ape_5.6-2            rotl_3.0.12          here_1.0.1          
## [37] maps_3.4.0           visdat_0.5.3         readxl_1.4.0        
## [40] forcats_0.5.1        stringr_1.4.0        dplyr_1.0.8         
## [43] purrr_0.3.4          readr_2.1.2          tidyr_1.2.0         
## [46] tibble_3.1.6         ggplot2_3.3.5        tidyverse_1.3.1     
## 
## loaded via a namespace (and not attached):
##   [1] estimability_1.3        coda_0.19-4             knitr_1.39             
##   [4] dygraphs_1.1.1.6        data.table_1.14.2       rpart_4.1.16           
##   [7] inline_0.3.19           generics_0.1.2          cowplot_1.1.1          
##  [10] callr_3.7.0             future_1.25.0           tzdb_0.3.0             
##  [13] webshot_0.5.4           xml2_1.3.3              httpuv_1.6.5           
##  [16] assertthat_0.2.1        xfun_0.30               hms_1.1.1              
##  [19] jquerylib_0.1.4         bayesplot_1.9.0         evaluate_0.15          
##  [22] promises_1.2.0.1        DEoptimR_1.0-11         fansi_1.0.3            
##  [25] progress_1.2.2          dbplyr_2.1.1            DBI_1.1.2              
##  [28] htmlwidgets_1.5.4       tensorA_0.36.2          stats4_4.2.0           
##  [31] ellipsis_0.3.2          crosstalk_1.2.0         backports_1.4.1        
##  [34] bookdown_0.26           markdown_1.1            RcppParallel_5.1.5     
##  [37] vctrs_0.4.1             abind_1.4-5             cachem_1.0.6           
##  [40] withr_2.5.0             robustbase_0.95-0       checkmate_2.1.0        
##  [43] treeio_1.21.0           xts_0.12.1              prettyunits_1.1.1      
##  [46] svglite_2.1.0           cluster_2.1.3           lazyeval_0.2.2         
##  [49] crayon_1.5.1            labeling_0.4.2          pkgconfig_2.0.3        
##  [52] nlme_3.1-157            vipor_0.4.5             nnet_7.3-17            
##  [55] rlang_1.0.2             globals_0.14.0          lifecycle_1.0.1        
##  [58] miniUI_0.1.1.1          colourpicker_1.1.1      ergm_4.1.2             
##  [61] cellranger_1.1.0        distributional_0.3.0    rprojroot_2.0.3        
##  [64] matrixStats_0.62.0      aplot_0.1.6             loo_2.5.1              
##  [67] carData_3.0-5           boot_1.3-28             zoo_1.8-10             
##  [70] reprex_2.0.1            base64enc_0.1-3         beeswarm_0.4.0         
##  [73] ggridges_0.5.3          processx_3.5.3          viridisLite_0.4.0      
##  [76] png_0.1-7               bitops_1.0-7            rncl_0.8.6             
##  [79] parallelly_1.31.1       rstatix_0.7.0           jpeg_0.1-9             
##  [82] shinystan_2.6.0         gridGraphics_0.5-1      ggsignif_0.6.3         
##  [85] scales_1.2.0            memoise_2.0.1           magrittr_2.0.3         
##  [88] plyr_1.8.7              threejs_0.3.3           compiler_4.2.0         
##  [91] rstantools_2.2.0        RColorBrewer_1.1-3      cli_3.3.0              
##  [94] listenv_0.8.0           ps_1.7.0                TMB_1.8.1              
##  [97] Brobdingnag_1.2-7       htmlTable_2.4.0         Formula_1.2-4          
## [100] mgcv_1.8-40             MASS_7.3-56             tidyselect_1.1.2       
## [103] stringi_1.7.6           highr_0.9               yaml_2.3.5             
## [106] svUnit_1.0.6            latticeExtra_0.6-29     bridgesampling_1.1-2   
## [109] grid_4.2.0              sass_0.4.1              tools_4.2.0            
## [112] parallel_4.2.0          rstudioapi_0.13         foreign_0.8-82         
## [115] gridExtra_2.3           trust_0.1-8             posterior_1.2.1        
## [118] rmdformats_1.0.3        farver_2.1.0            digest_0.6.29          
## [121] shiny_1.7.1             car_3.0-13              later_1.3.0            
## [124] httr_1.4.2              colorspace_2.0-3        rvest_1.0.2            
## [127] XML_3.99-0.9            fs_1.5.2                splines_4.2.0          
## [130] yulab.utils_0.0.4       tidytree_0.3.9          shinythemes_1.2.0      
## [133] ggplotify_0.1.0         systemfonts_1.0.4       xtable_1.8-4           
## [136] jsonlite_1.8.0          nloptr_2.0.0            lpSolveAPI_5.5.2.0-17.7
## [139] rle_0.9.2               ggfun_0.0.6             R6_2.5.1               
## [142] Hmisc_4.7-0             pillar_1.7.0            htmltools_0.5.2        
## [145] mime_0.12               glue_1.6.2              fastmap_1.1.0          
## [148] minqa_1.2.4             DT_0.22                 codetools_0.2-18       
## [151] pkgbuild_1.3.1          mvtnorm_1.1-3           furrr_0.2.3            
## [154] utf8_1.2.2              lattice_0.20-45         bslib_0.3.1            
## [157] network_1.17.1          numDeriv_2016.8-1.1     arrayhelpers_1.1-0     
## [160] curl_4.3.2              rentrez_1.2.3           HDInterval_0.2.2       
## [163] gtools_3.9.2            shinyjs_2.1.0           survival_3.3-1         
## [166] rmarkdown_2.14          statnet.common_4.5.0    munsell_0.5.0          
## [169] haven_2.5.0             reshape2_1.4.4          gtable_0.3.0
---
title: "The impact of rising temperatures on the prevalence of coral diseases and their predictability: a global meta-analysis"
author: 
date: "Latest update: `r format(Sys.time(), '%d %B %Y')`"
output: 
  rmdformats::readthedown:
    code_folding: show
    code_download: true
    toc_depth: 4
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
# Knitr settings
knitr::opts_chunk$set(
  message=FALSE, 
  warning=FALSE,
  tidy=TRUE,
  cache=TRUE,
  echo = TRUE)

```

This html documents the data calculation, cleaning, modeling, and visualization of a global meta-analysis of coral disease prevalence alongside rising ocean temperatures.


# **Data Wrangling and Calculation**

## Load Packages
```{r}
library(tidyverse)    # ggplot, dplyr, %>%, and friends
library(readxl)       # read in excel files
library(visdat)       # visualize missing data
library(maps)         # map visualization
library(here)         # read in data from project
library(rotl)         # connect with the Open Tree of Life
library(ape)          # for phylogenetic tree manipulation
library(RDS)          # save RDS files
library(BiocManager)  # install and manage Bioconductor packages
library(ggtree)       # devtools::install_github("YuLab-SMU/ggtree")
library(ggtreeExtra)  # devtools::install_github("xiangpin/ggtreeExtra")
library(ggnewscale)   # add colour layers in phylogenetic trees
library(R.utils)      # programming utilities
library(ncdf4)        # read ncdf files
library(lubridate)    # working with dates and times
library(RCurl)        # HTTP interface
library(birk)         # data summaries
library(lme4)         # linear mixed models
library(rstan)        # Stan models
library(glmmTMB)      # run small GLMMs quickly
library(modelr)       # pipelines
library(brms)         # bayesian modeling through Stan
library(tidybayes)    # manipulate Stan objects in a tidy way
library(broom)        # convert model objects to data frames
library(broom.mixed)  # convert brms model objects to data frames
library(emmeans)      # calculate marginal effects in even fancier ways
library(patchwork)    # combine ggplot objects
library(ggokabeito)   # neat accessible color palette
library(gghalves)     # special half geoms
library(ggbeeswarm)   # special distribution-shaped point jittering
library(ggdist)       # distribution visualisation
library(igraph)       # manually alter plots
library(ggpubr)       # manually alter plots
library(ggExtra)      # additional plot tools
library(kableExtra)   # for tables
```

## Load Data
Data was originally organized in an excel file where each sheet of the excel file contained different data with a common identifier between all (Effect Size ID and Paper ID).

Effect Size Data sheet contained information relevant to the effect size calculation (as believed to be relevant at start of project).

Bibliographic Data sheet contained details of the bibliographic information to identify paper and effect size IDs to proper publication.

Transect Data sheet contained information relevant to sample collection (i.e., survey method and dimensions).

Moderator Data sheet contained additional information not directly related to effect size calculation (e.g., Total Sample Area size, number of corals - if provided, etc.).

Disease Data sheet contained all the diseases identified in studies and recorded how many disease incorporated in each disease prevalence metric and which diseases are present ("1") or absent ("0") in that sample.

Species Data sheet contained information relevant to the species included in each study. Since most studies do not separate the disease prevalence metric by species, this data sheet was only linked to the others by Paper ID.
```{r data_load}
# Check that file isn't open in excel
# Read in each excel sheet
prevESD <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Effect Size Data")
bibdata <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Bibliographic Data")
prevTRAN <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Transect Data")
prevMOD <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Moderator Data")
prevDIS <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Disease Data")
prevSPP <- read_excel(path = here("data", "Coral Disease Extracted Data.xlsx"), sheet = "Real Species Data")

# List of included studies
papers <- subset(bibdata, Paper_ID!="")
```


## Organize Data
```{r, eval = F}
# visualize missing data
dat.list <- list(prevDIS, prevESD, prevMOD, prevSPP, prevTRAN)

missing<- lapply(dat.list, vis_miss)
missing
```

```{r}
# Remove data with no effect sizes
ESD <- subset(prevESD, Effect_ID!="")
MOD <- subset(prevMOD, Effect_ID!="")
TRAN <- subset(prevTRAN, Effect_ID!="")
DIS <- subset(prevDIS, Effect_ID!="")
```

```{r, eval = F}
# Only keep effect sizes measured as disease prevalence in %
percentonly <- subset(prevESD, Unit_Prevalence!="col/100m^2" & 
                        Unit_Prevalence!="col/m^2" & 
                        Unit_Prevalence!="mean no. colonies/m^2" & 
                        Unit_Prevalence!="no. col" & 
                        Effect_ID!="")

# Visualize missing data patterns
vis_miss(percentonly)
```

```{r, eval = F}
# The regions extracted from papers were too precise. Therefore, we redefined regions based on ocean basins
ocean_group <- function(Region_HoeghGuldberg) {
  case_when(
    Region_HoeghGuldberg == "Western Indian Ocean" | Region_HoeghGuldberg=="Eastern Indian Ocean"      ~ "Indian Ocean",
    Region_HoeghGuldberg=="Western Pacific" | Region_HoeghGuldberg=="Coral Triangle & SE Asia" ~ "Pacific Ocean",
    Region_HoeghGuldberg=="Caribbean & Gulf of Mexico" | Region_HoeghGuldberg=="XXXX" ~ "Atlantic Ocean"
  )
}

# Create a new column for oceans
percentonly$Ocean <- ocean_group(percentonly$Region_HoeghGuldberg)
```


## Extract Sea Surface Temperature (SST) Data {.tabset}
As most studies didn't provide the temperature data at the sample site, we needed to calculate this data using an external dataset. We used NOAA COBE2 available at [NOAA Physical Sciences Laboratory website](https://data.noaa.gov/dataset/dataset/cobe-sst2-sea-surface-temperature-and-ice)

### Load in SST data
```{r, eval = F}
# Get data from NOAA database
url = "ftp://ftp.cdc.noaa.gov/Datasets/COBE2/sst.mon.mean.nc"
bin = getBinaryURL(url)
writeBin(bin, "mon.sst.nc")
SSTData <- nc_open(filename = "mon.sst.nc")
rm(bin)
file.remove("mon.sst.nc")

# Set variables from dataset
lon <- ncvar_get(SSTData, varid = "lon")
lat <- ncvar_get(SSTData, varid = "lat")
time <- ncvar_get(SSTData, varid = "time")
sst <- ncvar_get(SSTData, varid = "sst")

# Set time as a date to match extracted data
dim(sst)
SSTData$dim$time$units
sst.date <- as.Date("1891-01-01") + time
```


### Create functions
Needed to extract SST, time, and location
```{r, eval = F}
# First define a helper function that extracts middle value (or average of 2 middle values) from a vector).
midval <- function(x) {
  if(length(x)%%2 == 1) {
    return(x[ceiling(length(x)/2)])
  } else return(mean(c(x[length(x)/2], x[length(x)/2 + 1]), na.rm = T))
  
}

# Define a function for extracting mean SST value at a particular location from a particular time period
sst_extract <- function(start_date, end_date, data_source,
                        lon_index, lat_index,
                        summer = F,
                        fun = mean, ...) {
  require("lubridate")
  if(summer) {
    if(lat_index <= 90) {
      month(start_date) <- 6
      end_date <- start_date
      month(end_date) <- 8
    } else if(lat_index > 90) {
      month(start_date) <- 12
      end_date <- start_date
      month(end_date) <- 2
      year(end_date) <- year(end_date) + 1
    }
  }
  
  start_date_units <- as.numeric(start_date - as.Date("1891-01-01"))
  end_date_units <- as.numeric(end_date - as.Date("1891-01-01"))
  start_date_index <- which(time == start_date_units)
  end_date_index <- which(time == end_date_units)
  
  # ...and extract data
  sst_extract <- data_source[lon_index, lat_index, seq(start_date_index, end_date_index)]
  # Here we assign data for i-th record to our list...
  
  return(fun(sst_extract, ...))
}


# Create function which finds the closest coordinate in SST dataset from our extracted dataset
find_close <- function(datasource, lon_ix, lat_ix, step = 3, summer = F, start_date, end_date, diagn = F) {
  lon_steps = seq(lon_ix - step, lon_ix + step)
  lat_steps = seq(lat_ix - step, lat_ix + step)
  
  longitudes = numeric(length(lon_steps)) + 1
  latitudes = numeric(length(lat_steps)) + 1
  names(longitudes) = lon_steps
  names(latitudes) = lat_steps
  
  for(i in 1:length(longitudes)) {
    
    sst_vals = sst_extract(start_date = start_date, end_date = end_date, data_source = datasource,
                           lat_index = lat_ix,
                           lon_index = as.numeric(names(longitudes)[i]),
                           summer = summer,
                           fun = function(x) return(x))
    longitudes[i] = any(!is.na(sst_vals))
    
  }
  
  for(i in 1:length(latitudes)) {
    
    sst_vals = sst_extract(start_date = start_date, end_date = end_date, data_source = datasource,
                           lon_index = lon_ix,
                           lat_index = as.numeric(names(latitudes)[i]),
                           summer = summer,
                           fun = function(x) return(x))
    latitudes[i] = any(!is.na(sst_vals))
    
  }
  
  if (diagn) {
    return(rbind(longitudes, latitudes))
  } else {
    
    if(any(longitudes == 1)) {
      for (k in 1:step) {
        if (longitudes[step + 1 + k] == 1) {
          lon_new = as.numeric(names(longitudes)[step + 1 + k])
          break
        } else if (longitudes[step + 1 - k] == 1) {
          lon_new = as.numeric(names(longitudes)[step + 1 - k])
          break
        }
      }
      lon_ix = lon_new
    } else {
      
      for (k in 1:step) {
        if (latitudes[step + 1 + k] == 1) {
          lat_new = as.numeric(names(latitudes)[step + 1 + k])
          break
        } else if (latitudes[step + 1 - k] == 1) {
          lat_new = as.numeric(names(latitudes)[step + 1 - k])
          break
        }
      }
      lat_ix = lat_new
      
    }
    
    
    return(c(lon_ix, lat_ix))
    
  }
}

# Create a function to identify survey season from sampling period
season_extract <- function(start_date, end_date, lat_index) {
  require('lubridate')
  
  N_seasons <- c(rep('win', 2), rep('spr', 3), rep('sum', 3), rep('aut', 3), 'win')
  S_seasons <- c(rep('sum', 2), rep('aut', 3), rep('win', 3), rep('spr', 3), 'sum')
  
  if (as.numeric(difftime(end_date, start_date)) < 100) {
    
    if (lat_index <= 90) {
      return(names(sort(table(N_seasons[month(start_date):month(end_date)]), decreasing = T))[1])
    } else if (lat_index > 90) {
      return(names(sort(table(S_seasons[month(start_date):month(end_date)]), decreasing = T))[1])
    }
  } else return('multi')
}


# Assign month names to number values
months <- c(Jan = "01", Feb = "02", Mar = "03",
            Apr = "04", May = "05", Jun = "06",
            Jul = "07", Aug = "08", Sep = "09",
            Oct = "10", Nov = "11", Dec = "12")

# Set extracted data as dataframe
percentonly <- as.data.frame(percentonly)

# Create columns for values to go into in dataframe
percentonly$average_SST <- NA
percentonly$middle_SST <- NA
percentonly$sd_SST <- NA
percentonly$start_month <- NA
percentonly$end_month <- NA
percentonly$average_SST_summer <- NA
percentonly$middle_SST_summer <- NA
percentonly$sd_SST_summer <- NA

# Allow coordinates to round to the nearest 0.5 degree to best match to SST dataset
coord_grid <- function(coord) floor(coord) + 0.5
```

### Calculate SST
```{r, eval = F}
# Rename in case any variables accidentally get changed, so original is saved
percentonly_ <- percentonly

for (i in 1:nrow(percentonly_)) {
  # cat(i); cat("\n")
  if (grepl("^[A-Z]{1}[a-z]{2}$",
            percentonly_[i, "Month"])) {
    # this condition looks for cases with one month
    
    start.month <- end.month <- percentonly_[i, "Month"]
    start.yr <- end.yr <- percentonly_[i, "Year"]
    
    
  } else if (grepl("^([A-Z]{1}[a-z]{2}), (?:[A-Z]{1}[a-z]{2}, )*([A-Z]{1}[a-z]{2})$",
                   percentonly_[i, "Month"])) {
    # this condition looks for lists of month separated by ", "
    
    start.month <- gsub("^([A-Z]{1}[a-z]{2}), (?:[A-Z]{1}[a-z]{2}, )*([A-Z]{1}[a-z]{2})$",
                        replacement = "\\1",
                        percentonly_[i, "Month"])
    end.month <- gsub("^([A-Z]{1}[a-z]{2}), (?:[A-Z]{1}[a-z]{2}, )*([A-Z]{1}[a-z]{2})$",
                      replacement = "\\2",
                      percentonly_[i, "Month"])
    start.yr <- percentonly_[i, "Year"]
    end.yr <- ifelse(which(names(months) == end.month) > which(names(months) == start.month),
                     start.yr, start.yr + 1)
    
  } else if (grepl("^([A-Z]{1}[a-z]{2})-[A-Z]{1}[a-z]{2}, (?:[A-Z]{1}[a-z]{2}-[A-Z]{1}[a-z]{2}, )*[A-Z]{1}[a-z]{2}-([A-Z]{1}[a-z]{2})$",
                   percentonly_[i, "Month"])) {
    # this condition looks for ranges of months signified by "-" in multiple ranges
    
    start.month <- gsub("^([A-Z]{1}[a-z]{2})-[A-Z]{1}[a-z]{2}, (?:[A-Z]{1}[a-z]{2}-[A-Z]{1}[a-z]{2}, )*[A-Z]{1}[a-z]{2}-([A-Z]{1}[a-z]{2})$",
                        replacement = "\\1",
                        percentonly_[i, "Month"])
    end.month <- gsub("^([A-Z]{1}[a-z]{2})-[A-Z]{1}[a-z]{2}, (?:[A-Z]{1}[a-z]{2}-[A-Z]{1}[a-z]{2}, )*[A-Z]{1}[a-z]{2}-([A-Z]{1}[a-z]{2})$",
                      replacement = "\\2",
                      percentonly_[i, "Month"])
    start.yr <- percentonly_[i, "Year"]
    end.yr <- ifelse(which(names(months) == end.month) > which(names(months) == start.month),
                     start.yr, start.yr + 1)
    
  } else if (grepl("^([A-Z]{1}[a-z]{2})-([A-Z]{1}[a-z]{2})$",
                   percentonly_[i, "Month"])) {
    # this condition looks for ranges of months signified by "-"
    
    start.month <- gsub("^([A-Z]{1}[a-z]{2})-([A-Z]{1}[a-z]{2})$",
                        replacement = "\\1",
                        percentonly_[i, "Month"])
    end.month <- gsub("^([A-Z]{1}[a-z]{2})-([A-Z]{1}[a-z]{2})$",
                      replacement = "\\2",
                      percentonly_[i, "Month"])
    start.yr <- percentonly_[i, "Year"]
    end.yr <- ifelse(which(names(months) == end.month) > which(names(months) == start.month),
                     start.yr, start.yr + 1)
    
  } else if (percentonly_[i, "Month"] == 0) {
    start.yr <- end.yr <- percentonly_[i, "Year"]
    if(percentonly_[i, "Lat"] > 0) start.month <- end.month <- "Jul"
    if(percentonly_[i, "Lat"] < 0) start.month <- end.month <- "Jan"
    
  } else start.month <- end.month <- middle.month <- -999
  
  if (start.month!= -999) {
    lat_index <- which(lat == coord_grid(percentonly_[i, "Lat"])) # ...extract relevant indexes...
    lon_index <- which(lon == coord_grid(ifelse(percentonly_[i, "Lon"] < 0,
                                                360 + percentonly_[i, "Lon"],
                                                percentonly_[i, "Lon"])))
    
    start_date <- as.Date(paste(start.yr, "-", months[start.month], "-01", sep = ""))
    end_date <- as.Date(paste(end.yr, "-", months[end.month], "-01", sep = ""))
    
    
    if(any(is.na(sst_extract(start_date, end_date, sst,
                             lon_index, lat_index, summer = F, function(x) return (x))))) {
      lon_index = find_close(datasource = sst, lon_ix = lon_index, lat_ix = lat_index,
                             step = 10, summer = F, start_date = start_date, end_date = end_date)[1]
      lat_index = find_close(datasource = sst, lon_ix = lon_index, lat_ix = lat_index,
                             step = 10, summer = F, start_date = start_date, end_date = end_date)[2]
    }
    
    percentonly_[i, "start_month"] <- as.numeric(format(start_date, "%m"))
    percentonly_[i, "end_month"] <- as.numeric(format(end_date, "%m"))
    
    percentonly_[i, "average_SST"] <- sst_extract(start_date, end_date, sst,
                                                  lon_index, lat_index, summer = F, mean, na.rm = T)
    percentonly_[i, "middle_SST"] <- sst_extract(start_date, end_date, sst,
                                                 lon_index, lat_index, summer = F, midval)
    percentonly_[i, "sd_SST"] <- sst_extract(start_date, end_date, sst,
                                             lon_index, lat_index, summer = F, sd, na.rm = T)
    percentonly_[i, "average_SST_summer"] <- sst_extract(start_date, end_date, sst,
                                                         lon_index, lat_index, summer = T, mean, na.rm = T)
    percentonly_[i, "middle_SST_summer"] <- sst_extract(start_date, end_date, sst,
                                                        lon_index, lat_index, summer = T, midval)
    percentonly_[i, "sd_SST_summer"] <- sst_extract(start_date, end_date, sst,
                                                    lon_index, lat_index, summer = T, sd, na.rm = T)
    percentonly_[i, "season"] <- season_extract(start_date, end_date, lat_index)
    
    
  } else if (start.month == -999) {
    
  } else stop("Error in month formatting\n")
}



# rename back to original once finished
percentonly <- percentonly_

## Check sst data coverage
out <- array(0, dim = c(360, 180, 2040))
plot(1:360, 1:360, type = "n", ylim = c(1,180))
for (i in 1:360) {
  for (j in 1:180) {
    for(k in 1:2040) {
      if(!is.na(sst[i,j,k])) {
        # points(lon[i], lat[j])
        out[i,j,k] <- out[i,j,k] + 1
      }
    }
  }
}

out1 <- rowSums(out, dims = 2)
dim(out1)
plot(1:360, 1:360, type = "n", ylim = c(180,1))
for (i in 1:360) {
  for(j in 1:180) {
    points(i, j, cex = 0.001 * out1[i,j])
  }
}

```

```{r, eval = F}
# Check if averages make sense with middle month values
ggplot(percentonly, aes(x = average_SST, y = middle_SST)) + geom_point()

# remove NaN results
TStemp <- subset(percentonly, (middle_SST == "NaN"))

```

### Check values
```{r, eval = F}
# Check correlation between year and average SST
cor.test(percentonly$Year, percentonly$average_SST)
ggplot(percentonly, mapping = aes(Year, average_SST, col = abs(Lat))) + 
  geom_jitter() +
  geom_smooth()


# Check by lat values without <25
ggplot(percentonly, mapping = aes(Year, middle_SST, col = abs(Lat))) + 
  geom_jitter() +
  geom_smooth()
ggplot(percentonly, mapping = aes(Year, average_SST, col = abs(Lat))) +
  geom_point() +
  geom_smooth(method = "lm")
ggplot(percentonly, mapping = aes(Year, average_SST_summer, col = abs(Lat))) +
  geom_point() +
  geom_smooth(method = "lm")
```


## Combine Data Sheets {-}
```{r, eval = F}
# Combine into one dataset
percentonly %>% left_join(MOD, by = c("Effect_ID", "Paper_ID")) %>% 
  left_join(TRAN, by = c("Effect_ID", "Paper_ID")) %>% 
  left_join(DIS, by = c("Effect_ID", "Paper_ID"))      -> dat_with_70s

rdsdat <- dat_with_70s
```


## New SST Database for WSSTA
We needed to utilize a finer resolution database to calculate Weekly Sea Surface Temperature Anomaly (WSSTA), so we chose the daily SST database available through Copernicus which spans from January 1981 to present. This can be accessed at [Copernicus Data](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-surface-temperature)

We downloaded all between July 1988 and June 2018, unzipped all files into one shared "sst" folder. This folder is separate from the R Project as the R Project was kept in a shared drive and this folder was too big to upload and move (166GB). The files are organized in this folder with the names given by the Copernicus download, which begins with the YearMonthDay of the recorded SST values (e.g., 20180630...).

### Load data
```{r, include = F, eval=F}

setwd("C:/Users/saman/Documents/sst")
# Open a connection to a sample file to see its attributes
nc_temp <- nc_open('./19880701120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_CDR2.0-v02.0-fv01.0.nc')
print(nc_temp)

# Example of extracting only some of a specific variable
varpart <- ncvar_get(nc_temp, "analysed_sst", start = c(1,50,1), count = c(-1,10,1))
# Is equivalent to this
varpart <- ncvar_get(nc_temp, "analysed_sst", start = c(1,50), count = c(-1,10))
# Dimensions are provided as X-Y-(Z)-T, and they are lon-lat-time

# Here I extract the allowed values of dimensions
lonvar <- ncvar_get(nc_temp, "lon")
latvar <- ncvar_get(nc_temp, "lat")

# Always try to close file connection asap
nc_close(nc_temp)

# Let's try to make a disc copy of a new file and fill it with 10 days of data
# First define dimensions
londim <- ncdim_def("longitude", "degrees_est", lonvar)
latdim <- ncdim_def("latitude", "degrees_north", latvar)
timedim <- ncdim_def("time", "days", 1, unlim = T)

# Now define variables
sstvar <- ncvar_def("sst", "kelvin", dim = list(londim, latdim, timedim), prec = "float")

# create new NC file
sstdata <- nc_create("sst.nc", list(sstvar))

# Write data from the first day
myfiles <- list.files("C:/Users/saman/Documents/sst")
nc_temp <- nc_open(paste('C:/Users/saman/Documents/sst/', myfiles[1], sep = ''))   

sst_temp <- ncvar_get(nc_temp, 'analysed_sst')
nc_close(nc_temp)

# Check variable
dim(sst_temp)

# Write data
ncvar_put(sstdata, "sst", sst_temp, start = c(1, 1, 1), count = c(-1, -1, 1))

# Cleanup
nc_close(sstdata)
rm(sst_temp)


# See how the new file loads
sst_new <- nc_open("sst.nc")
print(sst_new)
nc_close(sst_new)

# Write next days (so starting with day no. 2) up to 10 to the new nc file
myfiles <- list.files("./sst/")
for (file in myfiles) {   #to go through files as days and count 7 days, calculate average, and reset (worry about dates later)
  
  # Open the given sst data in the loop
  nc_temp <- nc_open(paste('./sst/', myfiles[file], sep = ''))
  sst_temp <- ncvar_get(nc_temp, 'analysed_sst')
  nc_close(nc_temp)
  
  # Open file to write
  sst_data <- nc_open("sst.nc", write = T)
  
  # Write data
  dat <- ncvar_put(sstdata, "sst", sst_temp, start = c(1, 1, file), count = c(-1, -1, 7))     #if count = 7, then average 1:7
  
  # Average past 7
  weekly_sst <- mean(dat)
  paste(weekly_sst)
  
  # Update the time variable with the new day
  ncvar_put(sstdata, "time", day, start = day, count = 1)
  
  # close connection before the next iteration and cleanup data
  nc_close(sstdata)
  rm(sst_temp)
  
}


# Check the updated file
sstdata <- nc_open("sst.nc")
print(sstdata)
nc_close(sstdata)

lon <- ncvar_get(sstdata, varid = "lon")
lat <- ncvar_get(sstdata, varid = "lat")
time <- ncvar_get(sstdata, varid = "time")
sst <- ncvar_get(sstdata, varid = "sst")

dim(sst)
SSTData$dim$time$units
sst.date <- as.Date("1891-01-01") + time

# Note lon is in degrees East. so if W, subtract from 360
lonindex <- which(lon == 119.5)
# Note lat is in degrees North. if S, use neg value
latindex <- which(lat == 8.5)
timeindex <- which(format(sst.date, "%Y-%m-%d") == "2015-04-01")

sst[lonindex,latindex,timeindex]

# TEST
# geo_coordinates to use as [lat, lon]
coord <- c(56.5, 17.5) # 52.5N, 19.5E sea temp close to Gotland
lat_index <- which(lat == coord[1])
lon_index <- which(lon == coord[2])

# Range of dates to use
start_date <- as.Date("2012-11-01")
end_date <- as.Date("2013-03-01")
start_date_units <- as.numeric(start_date - as.Date("1800-01-01"))
end_date_units <- as.numeric(end_date - as.Date("1800-01-01"))
start_date_index <- which(time == start_date_units)
end_date_index <- which(time == end_date_units)

seq(start_date_index, end_date_index)
# Here we can clearly see data are monthly (we obtain 5 values)
sst_extract <- sst[lon_index, lat_index, seq(start_date_index, end_date_index)]
sst_extract
mean(sstdata[1:3])

# Remember to reset working directory to project location
```


### Calculation for WSSTA
```{r, eval = F}
### load folder with daily sst files
nc_sst_day <- nc_open("D:/Sam's Lenovo/Documents/sst/19880702120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_CDR2.0-v02.0-fv01.0.nc")

#### creation of new NC files with weekly averages ----
# first define dimensions
lonvar <- ncvar_get(nc_sst_day, "lon")
latvar <- ncvar_get(nc_sst_day, "lat")
londim <- ncdim_def("longitude", "degrees_east", lonvar)
latdim <- ncdim_def("latitude", "degrees_north", latvar)
timedim <- ncdim_def("time", "seconds from 1981-1-1", 1, unlim = T)

# now define variables
sstvar <- ncvar_def("sst", "kelvin", dim = list(londim, latdim, timedim), prec = "float")

# create file
sstdata <- nc_create("sst_1988_2018.nc", list(sstvar))
# list individual day files
myfiles <- list.files("D:/Sam's Lenovo/Documents/sst")

# loop over files and fill new NC file
i <- 1 # day counter
j <- 1 # weeks counter
for (current_file in myfiles) {
  
  # open the given sst data in the loop
  nc_temp <- nc_open(paste("D:/Sam's Lenovo/Documents/sst/", current_file, sep = ''))
  sst_temp <- ncvar_get(nc_temp, 'analysed_sst')
  
  if(i == 1) {
    time_temp <- ncvar_get(nc_temp, 'time')
    sst_avg <- (1/7)*sst_temp
  } else {
    sst_avg <- sst_avg + (1/7)*sst_temp
  }
  
  rm(sst_temp)
  nc_close(nc_temp)
  
  if (i == 7) {
    
    # open file to write
    sstdata <- nc_open("sst_1988_2018.nc", write = T)
    
    # write data
    ncvar_put(sstdata, "sst", sst_avg, start = c(1, 1, j), count = c(-1, -1, 1))
    
    # update the time variable with the new day
    ncvar_put(sstdata, "time", time_temp, start = j, count = 1)
    
    # close connection before the next iteration and cleanup data
    nc_close(sstdata)
    rm(sst_avg)
    i <- 1
    j <- j + 1
    
  } else {
    
    i <- i + 1
    
  }
  
  cat("File "); cat(current_file); cat(" done \n")
}


#### extract and add monthly averages maxima ----
mmm <- nc_open(here("data",'ct5km_climatology_v3.1.nc'))
lons <- round(ncvar_get(mmm, 'lon'), 3)
lats <- round(ncvar_get(mmm, 'lat'), 3)

rdsdat$MMM <- NA

# loop over data to fill MMM climatology values
for (i in 1:nrow(rdsdat)) {
  
  if(rdsdat[i, "Year"] > 1989) {
    
    # rounding strategy to get correct rounding resolution
    lon_index <- which(lons == round(floor(rdsdat[i,'Lon']/0.05)*0.05 + 0.025, 3))
    lat_index <- which(lats == round(floor(rdsdat[i,'Lat']/0.05)*0.05 + 0.025, 3))
    
    # adding 273.15 turns Celsius into Kelvin
    mmm_val <- ncvar_get(mmm, 'sst_clim_mmm', 
                         start = c(lon_index, lat_index, 1),
                         count = c(1,1,1)) + 273.15
    
    rdsdat[i, 'MMM'] <- mmm_val
  }
  
  cat('Entry '); cat(i); cat(' done\n')
  
}

nc_close(mmm)


#### extract WSSTA weekly ----
sst <- nc_open('sst_1988_2018.nc')
lons <- round(ncvar_get(sst, 'longitude'), 3)
lats <- round(ncvar_get(sst, 'latitude'), 3)
time <- ncvar_get(sst, 'time')

rdsdat$WSSTA <- NA

for (i in 1:nrow(rdsdat)){
  if (any(is.na(rdsdat[i, "start_month"]))) {
    next
  }
  
  if(rdsdat[i, 'Year'] <= 1989) {
    next
  }
  
  sample.date <- as.Date(paste(rdsdat[i,"Year"], "-",
                               rdsdat[i,"start_month"], "-", "01", sep = ""))
  start.date <- as.numeric(sample.date - as.Date('1981-1-1'))*24*3600
  start.week <- time[which.closest(time, start.date)]
  end.week <- time[which.closest(time, (as.numeric(sample.date - as.Date('1981-1-1'))-365)*24*3600)]
  timewindow <- subset(time, end.week <= time & time <= start.week)
  end.week <- which(time == end.week)
  
  # Get all weekly values from SSTData for given coordinates and input into vector
  lon_index <- which(lons == round(floor(rdsdat[i,'Lon']/0.05)*0.05 + 0.025, 3))
  lat_index <- which(lats == round(floor(rdsdat[i,'Lat']/0.05)*0.05 + 0.025, 3))
  
  
  # Extract variables
  year.sst <- ncvar_get(sst, varid = "sst", start = c(lon_index,lat_index,end.week), count = c(1,1,length(timewindow)))
  # cat(year.sst); cat("\n")
  
  # Check if for every set of coordinates, there is a corresponding temperature
  if (all(is.nan(year.sst) == TRUE)) {
    cat('Entry '); cat(i); cat('needs coordinate adjustments') # Row number that needs coordinate checking for if land
    next
  }
  
  threshold <- rdsdat[i, 'MMM']
  
  # Calculate WSSTA with comparison to threshold
  heatweek <- c()
  for (k in 1:length(year.sst)) {   # k is an index for a position in year.sst
    hotspot.dev <- year.sst[k] - threshold
    if (is.nan(hotspot.dev) == TRUE | is.na(hotspot.dev) == TRUE){
      print("NA")
    }
    if (hotspot.dev > 1) {
      heatweek <- c(heatweek, hotspot.dev)
    } else {
      next   # distinguish between those that were not higher than threshold vs those that were NA or NaN
    }
  }
  rdsdat[i, "WSSTA"] <- sum(heatweek)
  
  cat('Entry '); cat(i); cat(' done\n')
}

nc_close(sst)



```


## Remove Missing Data
```{r, eval=FALSE}
# Remove pre-1992 data since WSSTA climatology is from 1985-1992 and don't want to compare values to themselves
dat_post92 <- subset(rdsdat, WSSTA != "Inf")

# Remove NAs
dat_WSSTA <- subset(dat_post92, WSSTA != "")
dat_Area <- subset(dat_WSSTA, Sample_Area_km2 != "")
dat_Tran <- subset(dat_Area, Transect_Type != "")
dat_DisNum <- subset(dat_Tran, Disease_Num != "")

# Rename data to something simpler
rdsdat <- dat_DisNum
```

```{r, include = F, eval = F}
#### write output table ----
write.table(rdsdat, 'CompletedData.csv', sep = ',')

saveRDS(rdsdat, here("data", "CompletedData.rds"))
```


# **Complete Dataset**

```{r}
rdsdat <- readRDS(here("data", "CompletedData.rds"))

kable(rdsdat) %>%  kable_styling("striped", position="left") %>% 
  scroll_box(width = "100%", height = "300px")
```


# **Data Exploration** {.tabset}

## Check for Erroneous Values
```{r}
# Check spread of disease prevalence values and ensure all fall in % ranges
countpercent <- count(rdsdat, Disease_Prevalence)
ggplot(countpercent) +
  geom_histogram(aes(x = Disease_Prevalence), color = "slateblue2", fill = "slateblue2") +
  labs(x = "Disease Prevalence (%)") +
  theme_bw()

```
Note large number of near 0% values


## Disease Prevalence
```{r}
# How many effect sizes report 0% disease prevalence?
count(rdsdat, Disease_Prevalence == 0)

# How many effect sizes report disease prevalence as a %?
count(ESD, Unit_Prevalence)
```


## Sample Areas
```{r, eval = F}
# Check spread of survey area values
areaonly <- subset(rdsdat, Sample_Area_km2!="")
countarea <- count(areaonly, Sample_Area_km2)
ggplot(areaonly) +
  geom_histogram(aes(x = Sample_Area_km2), color = "slateblue2", fill = "slateblue2")

# Scale survey area for better visualization
ggplot(areaonly) +
  geom_histogram(aes(x = Sample_Area_km2), color = "slateblue2", fill = "slateblue2")+
  scale_x_log10()
```


## Region Data
```{r}
# Count data per region
# count(rdsdat, Region_HoeghGuldberg)
# count(rdsdat, Region_Kleypas)
# ggplot(rdsdat) +
#   geom_bar(aes(x = Region_HoeghGuldberg))

ggplot(rdsdat) +
  geom_bar(aes(x = Ocean, color = Ocean, fill = Ocean)) +
  scale_color_viridis_d(end = 0.9) +
  scale_fill_viridis_d(end = 0.9) +
  theme_bw(base_size = 14) +
  theme(legend.position = "bottom")

```


## Year
```{r}
# Which years have the most data?
ggplot(rdsdat) +
  geom_bar(aes(x = Year), color = "slateblue2", fill = "slateblue2") +
  theme_bw()
```


## Survey Method
```{r}
# What are the most common sampling methods?
Transect_narm <- subset(rdsdat, is.na(rdsdat$Transect_Type) != TRUE)
ggplot(Transect_narm) +
  geom_bar(aes(x = Transect_Type), color = "slateblue2", fill = "slateblue2") +
  labs(x = "Transect Plot Type") +
  theme_bw()
```


## Map Visualizations
```{r, eval = F}
# Prototyping code - see Figure 1A for map

# Set map of world from existing map data
earth <- map_data("world")

# Plot map of survey locations on map
ggplot(rdsdat, aes(x = Lon, y = Lat)) +
  geom_map(data = earth,
           map = earth,
           aes(x = long, y = lat, group = group, map_id = region),
           fill = "white",
           colour = "black",
           size = 0.2) +
  geom_point(colour = 'blue', alpha = 0.25) +
  coord_quickmap()


# Plot survey locations colored by ocean
ggplot(rdsdat, aes(x = Lon, y = Lat, col = Ocean)) +
  geom_map(data = earth, 
           map = earth, 
           aes(x = long, y = lat, group = group, map_id = region), 
           fill = "grey85", 
           colour = "black", 
           size = 0.2) +
  geom_point(alpha = 0.25) +
  coord_quickmap() +
  labs(x = "Longitude", y = "Latitude") +
  theme_bw()


# Try plotting survey locations by disease number found at each site
# Too many values for number of disease. Try merging to just one vs more than one disease per effect size

# Write function to identify the value of Disease_Num
num_dis <- function(Disease_Num) {
  case_when(
    Disease_Num == 1   ~ "One Disease",
    Disease_Num != 1   ~ "Multiple Diseases"
  )
}

# Apply function to dataset
as.DiseaseNum <- rdsdat %>% mutate(type = num_dis(Disease_Num))

# Plot survey locations on map, colored by one or multiple diseases
ggplot(as.DiseaseNum, aes(x = Lon, y = Lat, col = type)) +
  geom_map(data = earth, map = earth, aes(x = long, y = lat, group = group, map_id = region), fill = "white", colour = "black", size = 0.2) +
  geom_point(alpha = 0.25) +
  coord_quickmap()
```


## Phylogenetic Tree
```{r, eval = F}
# Prototyping code - see Figure S7 for tree

genus <- unique(prevSPP$Genus[prevSPP$Genus != "Helioseris"]) # Removed the Genus Helioseris because it was removed from the analysed dataset because of the disease prevalence metric used
taxa_list <- na.omit(genus)
taxa_list2 <- as.factor(taxa_list)
taxa <- tnrs_match_names(names = levels(taxa_list2), context_name = "Animals") # Connect the list of Genera with the Open Tree taxonomy

taxa.in.tree <- ott_id(taxa)[is_in_tree(ott_id(taxa))] 

tree <- tol_induced_subtree(ott_ids = taxa.in.tree, label_format = "name")
tree$tip.label <- gsub("_\\(.*","", tree$tip.label) # Remove symbols

plain_tree <- plot(tree, cex = 0.5, label.offset = 0.1, no.margin = TRUE) 

is.binary(tree) # Check if binary
tree <- compute.brlen(tree, method = "Grafen", power = 1) # Compute branch lengths 

all_taxa <- intersect(as.character(tree$tip.label), taxa_list) # Find synonyms and typos and remove them
used_taxa <- setdiff(as.character(tree$tip.label), taxa_list)

# Build heatmap of oceans
species <- select(prevSPP, "PaperID", "Genus")
species$Paper_ID <- species$PaperID
dat.tree <- left_join(rdsdat, species, by = c("Paper_ID")) # Join the data with the Genus information
dat.tree <- select(dat.tree, Paper_ID, Genus, Ocean) # Reduce the data to the needed columns
dat.tree <- mutate(dat.tree, search_string = decapitalize(Genus)) # Decapitalize "Genus" to match the "search string" in the "taxa" file
dat.tree <- left_join(dat.tree, select(taxa, search_string, unique_name, ott_id), by = "search_string") # Join taxa information from rotl to the dataset

dat.tree$unique_name <- word(dat.tree$unique_name, 1) # Only keep one word for the Genus (we had e.g. "Stylophora (genus in phylum Cnidaria)")

dat.tree <- dat.tree[dat.tree$unique_name %in% tree$tip.label, ] # Check that the Genera match the ones used in the tree

ocean <- dat.tree %>% group_by(unique_name) %>% summarise( 
  oceanIndian = Ocean == "Indian Ocean", 
  oceanAtlantic = Ocean == "Atlantic Ocean",
  oceanPacific = Ocean == "Pacific Ocean")
ocean <- distinct(ocean) # Only keep unique rows
ocean$oceanIndian = as.numeric(ocean$oceanIndian) # convert TRUE/FALSE to binary values for each Ocean
ocean$oceanAtlantic = as.numeric(ocean$oceanAtlantic)
ocean$oceanPacific = as.numeric(ocean$oceanPacific)

ocean <- ocean %>% group_by(unique_name) %>% summarise(oceanIndian=sum(oceanIndian), # calculate the sum for each species (i.e., if 1, the species is found in the designated ocean)
                                                     oceanAtlantic=sum(oceanAtlantic),
                                                     oceanPacific=sum(oceanPacific))
ocean$oceanIndian=as.factor(ocean$oceanIndian) # Convert back to factor for the plot
ocean$oceanAtlantic=as.factor(ocean$oceanAtlantic)
ocean$oceanPacific=as.factor(ocean$oceanPacific)

# Plot Horizontal Tree
# simple phylogenetic tree
htree <- ggtree(tree, lwd = 1.05) + 
  geom_tiplab(offset = 0.04) # Display Genus

h <- htree %<+% ocean # Link plot to data

# plot tree and heatmap together
h2 <- h +  geom_fruit(geom=geom_tile, 
                     mapping=aes(fill=oceanAtlantic), 
                     width=0.075, 
                     offset=0.35) + 
  scale_fill_manual(name = "", values=c("white", "#7C4F85"), labels=c("", "Atlantic Ocean"), guide = guide_legend(order = 1)) +
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanIndian), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white","#71A8AF"), labels=c("", "Indian Ocean"), guide = guide_legend(order = 2))+
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanPacific), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white", "#D4E974"), labels=c("", "Pacific Ocean"), guide = guide_legend(order = 3))

h2
```


```{r, include = F, eval = F}
## Temperature Trends - delete
# Plot average summer temperature through time colored by ocean
ggplot(rdsdat, mapping = aes(Year, average_SST_summer, col = Ocean)) +
  geom_jitter(alpha = 0.5) +
  geom_smooth() + # remove this, think what's the best way to show this
  theme_bw() +
  theme() +
  labs(y = "Average Summer SST (\u00B0C)") +
  theme(legend.position = "bottom", legend.direction = "horizontal")

# Plot WSSTA through time colored by ocean
ggplot(rdsdat, mapping = aes(Year, WSSTA, col = Ocean)) +
  geom_jitter(alpha = 0.5) +
  geom_smooth() +
  theme_bw() +
  theme() +
  labs(y = "Weekly Sea Surface Temperature Anomaly (\u00B0C-weeks)") +
  theme(legend.position = "bottom", legend.direction = "horizontal")

```

## Disease Number and Types {.tabset}

### Spread of disease data
```{r}
# Visualize spread of disease number data
ggplot(rdsdat) +
  geom_bar(aes(x = Disease_Num), color = "slateblue2", fill = "slateblue2") +
  labs(x = "Disease Number") +
  theme_bw()

# Plot spread of effect sizes that report one or many diseases
ggplot() +
  geom_bar(rdsdat, mapping = aes(x = Disease_Num == 1), color = "slateblue2", fill = "slateblue2") +
  theme_bw() +
  labs(x = "Disease Number") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Multiple Diseases', 'One Disease'))


# summarize data by disease

# create dataset
Disease_Counts <- data.frame(WS = count(rdsdat, WS == 1)[2,2],
                             BBD = count(rdsdat, BBD == 1)[2,2],
                             GA = count(rdsdat, GA == 1)[2,2],
                             BrB = count(rdsdat, BrB == 1)[2,2],
                             SEB = count(rdsdat, SEB == 1)[2,2],
                             UWS = count(rdsdat, UWS == 1)[2,2],
                             TL = count(rdsdat, TL == 1)[2,2],
                             DSS = count(rdsdat, DSS == 1)[2,2],
                             WB = count(rdsdat, WB == 1)[2,2],
                             YBD = count(rdsdat, YBD == 1)[2,2],
                             WPx = count(rdsdat, WPx == 1)[2,2],
                             IMS = count(rdsdat, IMS == 1)[2,2],
                             Trema = count(rdsdat, Trema == 1)[2,2],
                             Cyano = count(rdsdat, Cyano == 1)[2,2],
                             PS = count(rdsdat, PS == 1)[2,2],
                             AN = count(rdsdat, AN == 1)[2,2],
                             PR = count(rdsdat, PR == 1)[2,2],
                             PUWS = count(rdsdat, PUWS == 1)[2,2],
                             DWS = count(rdsdat, DWS == 1)[2,2],
                             RBD = count(rdsdat, RBD == 1)[2,2],
                             STGA = count(rdsdat, STGA == 1)[2,2],
                             RM = count(rdsdat, RM == 1)[2,2],
                             RW = count(rdsdat, RW == 1)[2,2],
                             PLS = count(rdsdat, PLS == 1)[2,2],
                             PWPS = count(rdsdat, PWPS == 1)[2,2],
                             CT = count(rdsdat, CT == 1)[2,2],
                             PBTL = count(rdsdat, PBTL == 1)[2,2],
                             WPa = count(rdsdat, WPa == 1)[2,2],
                             Cilia = count(rdsdat, Cilia == 1)[2,2],
                             PBSS = count(rdsdat, PBSS == 1)[2,2],
                             GPD = count(rdsdat, GPD == 1)[2,2],
                             Unk = count(rdsdat, Unknown == 1)[2,2])

```

### Most common diseases by ocean {.tabset}
```{r}
#isolate data per ocean
Atlantic_Only <- subset(rdsdat, rdsdat$Ocean == "Atlantic Ocean")
Pacific_Only <- subset(rdsdat, rdsdat$Ocean == "Pacific Ocean")
Indian_Only <- subset(rdsdat, rdsdat$Ocean == "Indian Ocean")
```

#### White Syndrome
```{r}
WS_Atl <- ggplot() +
  geom_bar(Atlantic_Only, mapping = aes(x = Atlantic_Only$WS == 1), color = "grey80", fill = "grey80") +
  theme_bw() +
  labs(title = "Atlantic Ocean", x = "White Syndrome") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
WS_Pac <- ggplot() +
  geom_bar(Pacific_Only, mapping = aes(x = Pacific_Only$WS == 1), color = "grey80", fill = "grey80") +
  theme_bw() +
  labs(title = "Pacific Ocean", x = "White Syndrome") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
WS_Ind <- ggplot() +
  geom_bar(Indian_Only, mapping = aes(x = Indian_Only$WS == 1), color = "grey80", fill = "grey80") +
  theme_bw() +
  labs(title = "Indian Ocean", x = "White Syndrome") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
WS_Atl + WS_Pac + WS_Ind
```

#### Black Band Disease
```{r}
BBD_Atl <- ggplot() +
  geom_bar(Atlantic_Only, mapping = aes(x = Atlantic_Only$BBD == 1), color = "black", fill = "black") +
  theme_bw() +
  labs(title = "Atlantic Ocean", x = "Black Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
BBD_Pac <- ggplot() +
  geom_bar(Pacific_Only, mapping = aes(x = Pacific_Only$BBD == 1), color = "black", fill = "black") +
  theme_bw() +
  labs(title = "Pacific Ocean", x = "Black Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
BBD_Ind <- ggplot() +
  geom_bar(Indian_Only, mapping = aes(x = Indian_Only$BBD == 1), color = "black", fill = "black") +
  theme_bw() +
  labs(title = "Indian Ocean", x = "Black Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
BBD_Atl + BBD_Pac + BBD_Ind
```

#### Yellow Band Disease
```{r}
YBD_Atl <- ggplot() +
  geom_bar(Atlantic_Only, mapping = aes(x = Atlantic_Only$YBD == 1), color = "yellow", fill = "yellow") +
  theme_bw() +
  labs(title = "Atlantic Ocean", x = "Yellow Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
YBD_Pac <- ggplot() +
  geom_bar(Pacific_Only, mapping = aes(x = Pacific_Only$YBD == 1), color = "yellow", fill = "yellow") +
  theme_bw() +
  labs(title = "Pacific Ocean", x = "Yellow Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
YBD_Ind <- ggplot() +
  geom_bar(Indian_Only, mapping = aes(x = Indian_Only$YBD == 1), color = "yellow", fill = "yellow") +
  theme_bw() +
  labs(title = "Indian Ocean", x = "Yellow Band Disease") +
  theme(text = element_text(size = 14)) +
  scale_x_discrete(labels = c('Absent', 'Present'))
YBD_Atl + YBD_Pac + YBD_Ind
```
### {-}

```{r, include = F, eval = F}
## Heteroscedasticity - delete
# Plot average summer temperature and disease to check for homo/heteroscedasticity
ggplot(rdsdat, mapping = aes(average_SST_summer, Disease_Prevalence)) +
  geom_jitter(alpha=0.3) +
  geom_smooth() +
  labs(y = "Disease Prevalence (%)", x = "Average Summer SST (\u00B0C)") +
  theme_bw()
# and by ocean
ggplot(rdsdat, mapping = aes(average_SST_summer, Disease_Prevalence, col = Ocean)) +
  geom_jitter(alpha=0.3) +
  geom_smooth() +
  labs(y = "Disease Prevalence (%)", x = "Average Summer SST (\u00B0C)") +
  theme_bw()


# Plot WSSTA and disease to check for homo/heteroscedasticity
ggplot(rdsdat, mapping = aes(WSSTA, Disease_Prevalence)) +
  geom_jitter(alpha=0.3) +
  geom_smooth() +
  labs(y = "Disease Prevalence (%)", x = "WSSTA (\u00B0C-week)") +
  theme_bw()
# and by ocean
ggplot(rdsdat, mapping = aes(WSSTA, Disease_Prevalence, col = Ocean)) +
  geom_jitter(alpha=0.3) +
  geom_smooth() +
  labs(y = "Disease Prevalence (%)", x = "WSSTA (\u00B0C-week)") +
  theme_bw()


# Plot year and disease to check for homo/heteroscedasticity
ggplot(rdsdat, mapping = aes(Year, Disease_Prevalence)) +
  geom_jitter(alpha=0.3) +
  geom_smooth() +
  labs(y = "Disease Prevalence (%)") +
  theme_bw()
# and by ocean
ggplot(rdsdat, mapping = aes(Year, Disease_Prevalence, col = Ocean)) +
  geom_jitter(alpha=0.3) +
  geom_smooth() +
  labs(y = "Disease Prevalence (%)") +
  theme_bw()
```


```{r, include = F}
## Count by Paper
# Sort data by paper
by_paper <- rdsdat %>% 
  group_by(Paper_ID) %>% 
  slice_head(n = 1) %>% ungroup

# Count number of papers that survey each ocean
ggplot(by_paper) +
  geom_bar(aes(x = Ocean, color = Ocean, fill = Ocean)) +
  scale_color_viridis_d(end = 0.9) +
  scale_fill_viridis_d(end = 0.9) +
  theme_bw(base_size = 14) +
  theme(legend.position = "bottom")
```


## Hemisphere data {.tabset}

### North Hemisphere
```{r, eval = F}
# prototyping code

# Isolate North hemisphere values
poslat <- subset(rdsdat, Lat > "0") # Need to split into proper month format rather than the written format

# Table of North hemisphere values
table(poslat$start_month) -> Ncounts
# change table to dataframe
Ncounts <- as.data.frame(t(Ncounts))
# rename values to actual month name for axis values
Ncounts$Var2 <- c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')
# rename column for axis label
names(Ncounts)[2] <- 'Month'

# plot count of survey by start month in north
Nhemi <- ggplot(Ncounts) +
  geom_bar(aes(x=Month, y=Freq), color = "darkorchid3", fill = "darkorchid3", stat = 'identity') +
  theme_bw() +
  scale_x_discrete(limits = c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')) +
  labs(y = "North Freq") +
  theme(text = element_text(size = 15))
Nhemi
```

### South Hemisphere
```{r, eval = F}
# prototyping code

# Isolate South hemisphere values
neglat <- subset(rdsdat, Lat < "0")

# Table of South hemisphere values
Scounts <- table(neglat$start_month)
# Change table to dataframe
Scounts <- as.data.frame(t(Scounts))
# Rename values to actual month name for axis values
Scounts$Var2 <- c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')
# Rename column for axis label
names(Scounts)[2] <- 'Month'

# plot count of survey by start month in south
Shemi <- ggplot(Scounts) +
  geom_bar(aes(x=Month, y=Freq), color = "gold2", fill = "gold2", stat = 'identity') +
  theme(text = element_text(size = 50)) +
  theme_bw() +
  scale_y_reverse() +
  scale_x_discrete(limits = c('Jan', "Feb", 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'), position = "top") +
  labs(y = "South Freq")
Shemi
```

### Mirror Hemispheres {-}
```{r, eval = F}
# prototyping code

# Mirror view of Hemispheres
Nhemi/Shemi
```
## {-}

# **Meta-Analytic Models**

## Scale and Center Data
```{r}
# Change percentage to a proportion
rdsdat$Disease_P <-  (rdsdat$Disease_Prevalence)/100

# Scale predictors
rdsdat$sYear <- scale(rdsdat$Year)
rdsdat$sSumTemp <- scale(rdsdat$average_SST_summer)
rdsdat$sWSSTA <- scale(rdsdat$WSSTA)
rdsdat$sDisease_Num <- scale(rdsdat$Disease_Num)
rdsdat$logArea <- log(rdsdat$Sample_Area_km2*10e5)

# Center predictors
rdsdat$cYear <- scale(rdsdat$Year, scale = F)
rdsdat$cSumTemp <- scale(rdsdat$average_SST_summer, scale = F)

```


## Run Models {.tabset}
These models take very long to run and are too big to send through to GitHub. We have provided these models for download here.

### Model with no interaction between key variables (WSSTA, SST, and Year)
```{r, eval=FALSE}
no_interaction_f <- brmsformula(
  Disease_P|weights(logArea) ~ # estimates weighed by the logarithm of the sample area
    sYear + # Scaled sample year
    sSumTemp +  # Scaled summer temperature
    sWSSTA + # Scaled weekly sea surface temperature anomaly
    sDisease_Num + # Scaled number of diseases
    Ocean + # Ocean basin
    (1| Site_ID) + # Site as a random factor
    (1|Paper_ID) + # Study as a random factor
    (1|season) + # Season as a random factor
    (1|Transect_Type), # Type of transect as a random factor
  zi ~ 1 + sSumTemp + sWSSTA + sYear, # zi = zero-inflation
  phi ~ 1 + sSumTemp + sWSSTA + sYear) # phi = heteroscedasticity

no_interaction <- brm(no_interaction_f,
                      chains = 2,
                      iter = 30000,
                      warmup = 28000,
                      data = rdsdat,
                      family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                      control = list(adapt_delta = 0.95))
```

```{r}
no_interaction <- readRDS(here("Rdata","no_interaction_mod.rds"))

summary(no_interaction)
```


### Model with interactions between all key variables and Ocean
```{r, eval=FALSE}
all_interaction_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp*Ocean + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

all_interaction <- brm(all_interaction_f,
                       chains = 2,
                       iter = 30000,
                       warmup = 28000,
                       data = rdsdat,
                       family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                       control = list(adapt_delta = 0.95))
```

```{r}
all_interaction <- readRDS(here("Rdata","all_interaction_mod.rds"))

summary(all_interaction)
```


### Model with interaction between Year and Ocean and SST and Ocean only
```{r, eval=FALSE}
YearxOcean_SSTxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp*Ocean + 
    sWSSTA +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

YearxOcean_SSTxOcean <- brm(YearxOcean_SSTxOcean_f,
                            chains = 2,
                            iter = 30000,
                            warmup = 28000,
                            data = rdsdat,
                            family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                            control = list(adapt_delta = 0.95))
```

```{r}
YearxOcean_SSTxOcean <- readRDS(here("Rdata","YearxOcean_SSTxOcean_mod.rds"))

summary(YearxOcean_SSTxOcean)
```


### Model with interaction between SST and Ocean and WSSTA and Ocean only
```{r, eval=FALSE}
SSTxOcean_WSSTAxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear +
    sSumTemp*Ocean + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

SSTxOcean_WSSTAxOcean <- brm(SSTxOcean_WSSTAxOcean_f,
                             chains = 2,
                             iter = 30000,
                             warmup = 28000,
                             data = rdsdat,
                             family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                             control = list(adapt_delta = 0.95))
```

```{r}
SSTxOcean_WSSTAxOcean <- readRDS(here("Rdata","SSTxOcean_WSSTAxOcean_mod.rds"))

summary(SSTxOcean_WSSTAxOcean)
```


### Model with interaction between Year and Ocean and WSSTA and Ocean only
```{r, eval=FALSE}
YearxOcean_WSSTAxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

YearxOcean_WSSTAxOcean <- brm(YearxOcean_WSSTAxOcean_f,
                              chains = 2,
                              iter = 30000,
                              warmup = 28000,
                              data = rdsdat,
                              family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                              control = list(adapt_delta = 0.95))
```

```{r}
YearxOcean_WSSTAxOcean <- readRDS(here("Rdata","YearxOcean_WSSTAxOcean_mod.rds"))

summary(YearxOcean_WSSTAxOcean)
```


### Model with interaction between Year and Ocean only
```{r, eval=FALSE}
YearxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear*Ocean +
    sSumTemp + 
    sWSSTA +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

YearxOcean <- brm(YearxOcean_f,
                  chains = 2,
                  iter = 30000,
                  warmup = 28000,
                  data = rdsdat,
                  family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                  control = list(adapt_delta = 0.95))
```

```{r}
YearxOcean <- readRDS(here("Rdata","YearxOcean_mod.rds"))

summary(YearxOcean)
```


### Model with interaction between WSSTA and Ocean only
```{r, eval=FALSE}
WSSTAxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear +
    sSumTemp + 
    sWSSTA*Ocean +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

WSSTAxOcean <- brm(WSSTAxOcean_f,
                   chains = 2,
                   iter = 30000,
                   warmup = 28000,
                   data = rdsdat,
                   family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                   control = list(adapt_delta = 0.95))
```

```{r}
WSSTAxOcean <- readRDS(here("Rdata","WSSTAxOcean_mod.rds"))

summary(WSSTAxOcean)
```


### Model with interaction between SST and Ocean only
```{r, eval=FALSE}
SSTxOcean_f <- brmsformula(
  Disease_P|weights(logArea) ~
    sYear +
    sSumTemp*Ocean + 
    sWSSTA +
    sDisease_Num +
    (1|Site_ID) +
    (1|Paper_ID) +
    (1|season) +
    (1|Transect_Type),
  zi ~ 1 + sSumTemp + sWSSTA + sYear,
  phi ~ 1 + sSumTemp + sWSSTA + sYear)

SSTxOcean <- brm(SSTxOcean_f,
                 chains = 2,
                 iter = 30000,
                 warmup = 28000,
                 data = rdsdat,
                 family = zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit"),
                 control = list(adapt_delta = 0.95))
```

```{r}
SSTxOcean <- readRDS(here("Rdata","SSTxOcean_mod.rds"))

summary(SSTxOcean)
```
## {-}


# **Model Comparisons** {.tabset}

## Leave-One-Out Comparisons
```{r}
# Fit Models for LOO Comparison 
fit_no_interaction <- add_criterion(no_interaction, "loo")
fit_all_interaction <- add_criterion(all_interaction, "loo")
fit_YearxOcean_SSTxOcean <- add_criterion(YearxOcean_SSTxOcean, "loo")
fit_SSTxOcean_WSSTAxOcean <- add_criterion(SSTxOcean_WSSTAxOcean, "loo")
fit_YearxOcean_WSSTAxOcean <- add_criterion(YearxOcean_WSSTAxOcean, "loo")
fit_YearxOcean <- add_criterion(YearxOcean, "loo")
fit_WSSTAxOcean <- add_criterion(WSSTAxOcean, "loo")
fit_SSTxOcean <- add_criterion(SSTxOcean, "loo")


# LOO Comparison
lootest <- loo_compare(fit_SSTxOcean, 
                       fit_all_interaction, 
                       fit_no_interaction, 
                       fit_YearxOcean_SSTxOcean, 
                       fit_SSTxOcean_WSSTAxOcean, 
                       fit_YearxOcean_WSSTAxOcean, 
                       fit_YearxOcean, 
                       fit_WSSTAxOcean, 
                       criterion = "loo", 
                       model_names = NULL)

print(lootest, simplify = F)
```

## WAIC Comparisons
```{r}
# WAIC Comparison 
waic(no_interaction)
waic(all_interaction)
waic(YearxOcean_SSTxOcean)
waic(SSTxOcean_WSSTAxOcean)
waic(YearxOcean_WSSTAxOcean)
waic(YearxOcean)
waic(WSSTAxOcean)
waic(SSTxOcean)

# p_waic estimates greater than 0.4, use LOO instead for comparison
```
# {-}
We find the no_interaction and YearxOcean_SSTxOcean interaction models to be the best fits


# **Unscale Data**
We need means and standard deviation for WSSTA, SumTemp, and Year to convert out of scale
```{r}

meanSD <- rdsdat %>% summarise(Year_mean = mean(Year,na.rm = T),
                               Year_SD  = sd(Year,na.rm = T),
                               WSSTA_mean  = mean(WSSTA,na.rm = T),
                               WSSTA_SD  = sd(WSSTA,na.rm = T),
                               SumTemp_mean  = mean(average_SST_summer,na.rm = T),
                               SumTemp_SD = sd(average_SST_summer,na.rm = T)
)

meanSD
```


# **Future Predictions of Disease Prevalence** {.tabset}
## 2018
```{r}
# last year of data
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2018-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
```


## 2022
```{r}
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2022-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
```


## 2050
```{r}
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2050-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
```


## 2100
```{r}
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2100-meanSD$Year_mean)/meanSD$Year_SD),
          epred = TRUE,
          re_formula = NA)
```


## 2100 with RCP 8.5
```{r}
# 2015 IPCC RCP 8.5 "business as usual" predictions
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2100-meanSD$Year_mean)/meanSD$Year_SD, sSumTemp = (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD),
          epred = TRUE,
          re_formula = NA)
```


## Average summer SST alone increasing to RCP 8.5 levels
```{r}
# effect of if the Year didn't accelerate past last data point, and only average summer temperature changed
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2018-meanSD$Year_mean)/meanSD$Year_SD, sSumTemp = (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD),
          epred = TRUE,
          re_formula = NA)
```


## WSSTA Doubles
```{r}
# Effect of WSSTA independent of year and summer temperature
# assume arbitrarily that anomalies will get 2x more intense in future
# keeping year at 2018 so we don't account for any predicted changes in temperature that would occur when we predict the future years
no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = (2018-meanSD$Year_mean)/meanSD$Year_SD, sWSSTA = ((meanSD$WSSTA_mean*2)-meanSD$WSSTA_mean)/meanSD$WSSTA_SD),
          epred = TRUE,
          re_formula = NA)
```
# {-}

# **Figures**

## Figure 1 {.tabset}
Location data characteristics

### Figure 1A 

```{r, fig.height=12, fig.width=15}
earth <- map_data("world") # World map

# Map of survey locations, colored by Ocean basin
OceanMap <- ggplot() +
  geom_map(data = earth, 
           map = earth, 
           aes(x = long, y = lat, group = group, map_id = region), 
           fill = "grey95", 
           colour = "grey65", 
           size = 0.2) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_manual(breaks = c("TRUE", "FALSE"), values = c("darkorchid3", "gold2")) +
  scale_x_continuous(breaks = c(-180,-120,-60,0,60,120,180)) +
  scale_y_continuous(breaks = c(-66,-23,0,23,66), limits = c(-77,77)) +
  geom_point(data = rdsdat, aes(x = Lon, y = Lat, fill = Ocean,
                       colour = as.factor(rdsdat$Lat > 0)),
             alpha = 0.4, colour = "grey50",
             size = 4, shape = 21) +
  guides(fill = "legend") +
  #coord_quickmap() +
  labs(x = "Longitude", y = "Latitude") +
  theme_bw(base_size = 20) +
  theme(panel.grid.minor = element_blank()) +
  theme (legend.position = "none")

# Histogram of surveys by latitude
OceanMapM <- ggMarginal(OceanMap, type = "hist", margins = "y", size = 10,
                        bins = 31, fill = "grey40",
                        colour = "white")

```

### Figure 1B 

```{r, fig.height=12, fig.width=15}
# Subset Northern Hemisphere data
poslat <- subset(rdsdat, Lat > "0") 

# Table for Northern Hemisphere values
table(poslat$start_month) -> Ncounts
# Change table to dataframe
Ncounts <- as.data.frame(t(Ncounts))
# Rename values to actual month name for axis values
Ncounts$Var2 <- c('Jan', 
                  "Feb", 
                  'Mar', 
                  'Apr', 
                  'May', 
                  'Jun', 
                  'Jul', 
                  'Aug', 
                  'Sep', 
                  'Oct', 
                  'Nov', 
                  'Dec')
# Rename column for axis label
names(Ncounts)[2] <- 'Month'


# Subset Southern Hemisphere data
neglat <- subset(rdsdat, Lat < "0")

# Table for Southern Hemisphere values
Scounts <- table(neglat$start_month)
# Change table to dataframe
Scounts <- as.data.frame(t(Scounts))
# Rename values to actual month name for axis values
Scounts$Var2 <- c('Jan', 
                  "Feb", 
                  'Mar', 
                  'Apr', 
                  'May', 
                  'Jun', 
                  'Jul', 
                  'Aug', 
                  'Sep', 
                  'Oct', 
                  'Nov', 
                  'Dec')
# Rename column for axis label
names(Scounts)[2] <- 'Month'


# Plot number of estimates for each month per Hemisphere
Hemi <- ggplot(Scounts) +
  geom_bar(aes(x=Month, y=-1*Freq), 
           color = "gold2", 
           fill = "gold2", 
           stat = 'identity') +
  theme_bw(base_size = 20) +
  theme(panel.grid.minor = element_blank()) +
  geom_bar(data = Ncounts, aes(x=Month, y=Freq),
           stat = 'identity',
           color = "darkorchid3",
           fill = "darkorchid3") +
  scale_y_continuous(breaks = c(-50,0,50,100,150,200),
                     labels = c(50,0,50,100,150,200)) +
  scale_x_discrete(limits = c('Jan', 
                              "Feb", 
                              'Mar', 
                              'Apr', 
                              'May', 
                              'Jun', 
                              'Jul', 
                              'Aug', 
                              'Sep', 
                              'Oct', 
                              'Nov', 
                              'Dec'),
                   labels = c('J','F','M','A','M','J','J','A','S','O','N','D'),
                   position = "bottom") +
  labs(x = "", y = "No. of estimates in N/S Hemisphere")

```


### Combine plots {.active}

```{r, fig.height=12, fig.width=15}
ggarrange(OceanMapM, Hemi, widths = c(3,1), labels = c("A","B"))

```
## {-}

## Figure 2 {.tabset}
Changes in disease prevalence over the three factors: average summer sea surface temperature (SST) in °C, weekly sea surface temperature anomaly (WSSTA) in °C-weeks, and Year

```{r, results = 'hide'}
# Colour palette
palette()

# getting data from the model
dat <- no_interaction$data
```

### Figure 2A 
```{r, fig.height=10, fig.width=15}

# summer temp

# plotting lines
means_fixed <- no_interaction %>% 
  emmeans(~ sSumTemp,
          at = list(sSumTemp = seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% as_tibble() %>% 
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

#adding rSumTemp to dat
dat <- dat %>% 
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

# putting bubbles in plot
temp1 <- ggplot()+
  # confidence interval
  geom_smooth(data = means_fixed, aes(x = rSumTemp, y = lower.HPD), method =  "loess", formula = y~x, se = FALSE, lty =  "dotted", lwd = 0.5,  col = "black") +
  geom_smooth(data = means_fixed, aes(x = rSumTemp, y = upper.HPD), method =  "loess", formula = y~x, se = FALSE, lty = "dotted", lwd = 0.5,  col = "black") +
  # main line
  geom_smooth(data = means_fixed, aes(x = rSumTemp, y = emmean), method =  "loess", formula = y~x, se = FALSE, lwd = 1, col = "black") +
  geom_point(data = dat, aes(x = rSumTemp, y = Disease_P, size = logArea, fill = Ocean), shape = 21, alpha = 0.5) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_viridis_d(end = 0.9) +
  #facet_grid(rows = vars(condition)) +
  labs(y = "Proportion of disease prevalence", x = "Average Summer SST (\u00B0C)", size = expression(paste("log(Area Examined [", cm^2, "])", sep = "")), fill = "Ocean") +
  ylim(0, 1.0) + xlim(24.5, 32.5) +
  # themes
  theme_bw(base_size = 12) +  theme(legend.position="bottom", legend.box="vertical", legend.margin=margin())



```

### Figure 2B
```{r, fig.height=12, fig.width=15}
# plotting gradients
means_draws <- no_interaction %>% 
  emmeans(~ sSumTemp,
          at = list(sSumTemp = seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% 
  gather_emmeans_draws() %>%
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

temp2 <- ggplot(means_draws, aes(x = rSumTemp, y = .value)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greys") +
  labs(x = "Average Summer SST (\u00B0C)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  ylim(0, 0.25) + xlim(24.5, 32.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

```

### Figure 2C 
```{r, fig.height=12, fig.width=15}
# comparing min and max point  
trends_draws <- no_interaction %>% 
  emtrends(~ sSumTemp, var = "sSumTemp",
           at = list(sSumTemp = c((25-meanSD$SumTemp_mean)/meanSD$SumTemp_SD, (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD)), # 25 and 32 degree
           regrid = "response") %>% 
  gather_emmeans_draws() %>%
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

# plot
temp3 <- ggplot(trends_draws, aes(x = .value, fill = factor(rSumTemp))) +
  geom_vline(xintercept = 0) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               slab_alpha = 0.75) +
  scale_fill_viridis_d(option = "magma", begin = 0.4, end = 0.8) +
  labs(x = "Average marginal effects at the two temperatures below", 
       y = "Density", fill = "rSumTemp",
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom") +
  labs(fill = "Average Summer SST (\u00B0C)")


```

### Figure 2D 
```{r, fig.height=12, fig.width=15}
# WSSTA

# plotting lines
means_fixed <- no_interaction %>% 
  emmeans(~ sWSSTA,
          at = list(sWSSTA = seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% as_tibble() %>% 
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))

#adding rWSSTA to dat
dat <- dat %>% 
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))


wssta1 <- ggplot()+
  # confidence interval
  geom_smooth(data = means_fixed, aes(x = rWSSTA, y = lower.HPD), method =  "loess", formula = y~x, se = FALSE, lty =  "dotted", lwd = 0.5,  col = "black") +
  geom_smooth(data = means_fixed, aes(x = rWSSTA, y = upper.HPD), method =  "loess", formula = y~x, se = FALSE, lty = "dotted", lwd = 0.5,  col = "black") +
  # main line
  geom_smooth(data = means_fixed, aes(x = rWSSTA, y = emmean), method =  "loess", formula = y~x, se = FALSE, lwd = 1, col = "black") +
  geom_point(data = dat, aes(x = rWSSTA, y = Disease_P, size = logArea, fill = Ocean), shape = 21, alpha = 0.5) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_viridis_d(end = 0.9) +
  labs(y = "Proportion of disease prevalence", x = "WSSTA (\u00B0C-weeks)",
       fill = "Ocean",
       size = expression(paste("log(Area  Examined[", cm^2, "])", sep = ""))) +
  ylim(0, 1.0) + xlim(0, 3.5) +
  theme_bw(base_size = 12) +  theme(legend.position="bottom", legend.box="vertical", legend.margin=margin()) +
  guides(size = guide_legend(order = 1), fill = guide_legend(order = 2))
```

### Figure 2E
```{r, fig.height=12, fig.width=15}

# plotting gradients
means_draws <- no_interaction %>% 
  emmeans(~ sWSSTA,
          at = list(sWSSTA = seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% 
  gather_emmeans_draws() %>%
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))



wssta2 <- ggplot(means_draws, aes(x = rWSSTA, y = .value)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greys") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  ylim(0, 0.25) + xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")
```

### Figure 2F
```{r, fig.height=12, fig.width=15}

# comparing min and max point  
trends_draws <- no_interaction %>% 
  emtrends(~ sWSSTA, var = "sWSSTA",
           at = list(sWSSTA = c((0.4-meanSD$WSSTA_mean)/meanSD$WSSTA_SD, (4.3-meanSD$WSSTA_mean)/meanSD$WSSTA_SD)), 
           regrid = "response") %>% 
  gather_emmeans_draws() %>%
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))


wssta3 <- ggplot(trends_draws, aes(x = .value, fill = factor(rWSSTA))) +
  geom_vline(xintercept = 0) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               slab_alpha = 0.75) +
  scale_fill_viridis_d(option = "magma", begin = 0.4, end = 0.8) +
  labs(x = "Average marginal effect at the two WSSTA values below", 
       y = "Density", fill = "rWSSTA",
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom") +
  labs(fill = "WSSTA (\u00B0C-weeks)")
```

### Figure 2G 
```{r, fig.height=12, fig.width=15}

# year

# plotting lines
means_fixed <- no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = seq(min(dat$sYear),max(dat$sYear),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% as_tibble() %>% 
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

#adding rYear to dat
dat <- dat %>% 
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

# putting bubbles
year1 <- ggplot()+
  # confidence interval
  geom_smooth(data = means_fixed, aes(x = rYear, y = lower.HPD), method =  "loess", formula = y~x, se = FALSE, lty =  "dotted", lwd = 0.5,  col = "black") +
  geom_smooth(data = means_fixed, aes(x = rYear, y = upper.HPD), method =  "loess", formula = y~x, se = FALSE, lty = "dotted", lwd = 0.5,  col = "black") +
  # main line
  geom_smooth(data = means_fixed, aes(x = rYear, y = emmean), method =  "loess", formula = y~x, se = FALSE, lwd = 1, col = "black") +
  geom_point(data = dat, aes(x = rYear, y = Disease_P, size = logArea, fill = Ocean), shape = 21, alpha = 0.5) +
  scale_fill_viridis_d(end = 0.9) +
  scale_colour_viridis_d(end = 0.9) + 
  #facet_grid(rows = vars(condition)) +
  labs(y = "Proportion of disease prevalence", x = "Year", 
       fill = "Ocean", 
       size = expression(paste("log(Area Examined [", cm^2, "])", sep = ""))) +
  ylim(0, 1.0) + xlim(1990, 2020) +
  # themes
  theme_bw(base_size = 12) +  theme(legend.position="bottom", legend.box="vertical", legend.margin=margin())
```

### Figure 2H 
```{r, fig.height=12, fig.width=15}

# plotting gradients
means_draws <- no_interaction %>% 
  emmeans(~ sYear,
          at = list(sYear = seq(min(dat$sYear),max(dat$sYear),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>% 
  gather_emmeans_draws() %>%
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

year2 <- ggplot(means_draws, aes(x = rYear, y = .value)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greys") +
  labs(x =  "Year", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  ylim(0, 0.25) + xlim(1990, 2020) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

```

### Figure 2I
```{r, fig.height=12, fig.width=15}

# comparing min and max point  
trends_draws <- no_interaction %>% 
  emtrends(~ sYear, var = "sYear",
           at = list(sYear = c((1992-meanSD$Year_mean)/meanSD$Year_SD, (2018-meanSD$Year_mean)/meanSD$Year_SD)),
           regrid = "response") %>% 
  gather_emmeans_draws() %>%
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

year3 <- ggplot(trends_draws, aes(x = .value, fill = factor(rYear)), ) +
  geom_vline(xintercept = 0) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               slab_alpha = 0.75) +
  scale_fill_viridis_d(option = "magma", begin = 0.4, end = 0.8) +
  labs(x = "Average marginal effects at the two years below", 
       y = "Density", fill = "rYear",
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom") +
  labs(fill = "Year")

```

### Combine plots {.active}
```{r, fig.height=12, fig.width=15}

(temp1 + temp2 + temp3) / 
  (wssta1  + wssta2  + wssta3)   / 
  (year1 + year2 + year3) + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))
```
## {-}

## Figure 3 {.tabset}
Global disease prevalence prediction depicted three ways

### Figure 3A
```{r, fig.height=12, fig.width=15}
# summer temp

# new data range
sSumTemp_seq <- seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)

# creating new data
new_st<- expand_grid(
  sYear = 0,
  sSumTemp = sSumTemp_seq,
  sWSSTA = 0,
  sDisease_Num = 0,
  Ocean = c("Atlantic Ocean","Pacific Ocean","Indian Ocean") # this is for Atlantic
)


res_draws <- no_interaction %>% 
  epred_draws(newdata = new_st, dpar = c("mu", "zi", "phi"), re_formula = NA) %>% 
  group_by(sSumTemp, .draw) %>% summarise(sSumTemp = mean(sSumTemp),
                                          rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean),
                                          predicted = mean(.epred),
                                          mu = mean(mu),
                                          zi = mean(zi),
                                          phi = mean(phi)) 

# mu
temp_mu <- ggplot(res_draws, aes(x = rSumTemp, y = mu)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Reds") +
  labs(x =  "Average Summer SST (\u00B0C)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
```

### Figure 3B
```{r, fig.height=12, fig.width=15}

# zi
temp_zi <- ggplot(res_draws, aes(x = rSumTemp, y = zi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Blues") +
  labs(x =  "Average Summer SST (\u00B0C)", y = "Proportion of 0 disease prevalence",
       fill = "Credible interval") +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")

```

### Figure 3C
```{r, fig.height=12, fig.width=15}

# phi
temp_phi <- ggplot(res_draws, aes(x = rSumTemp, y = phi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greens") +
  labs(x =  "Average Summer SST (\u00B0C)", y = "Precison (1/SE) of disease prevalence",
       fill = "Credible interval") +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
```

### Figure 3D
```{r, fig.height=12, fig.width=15}

# WSSTA

# new data range
sWSSTA_seq <- seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)

# creating new data
new_wssta<- expand_grid(
  sYear = 0,
  sSumTemp = 0,
  sWSSTA = sWSSTA_seq,
  sDisease_Num = 0,
  Ocean = c("Atlantic Ocean","Pacific Ocean","Indian Ocean") # this is for Atlantic
)


res_draws <- no_interaction %>% 
  epred_draws(newdata = new_wssta, dpar = c("mu", "zi", "phi"), re_formula = NA) %>% 
  group_by(sWSSTA, .draw) %>% summarise(sWSSTA = mean(sWSSTA),
                                        rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean),
                                        predicted = mean(.epred),
                                        mu = mean(mu),
                                        zi = mean(zi),
                                        phi = mean(phi))

# mu
wssta_mu <- ggplot(res_draws, aes(x = rWSSTA, y = mu)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Reds") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Proportion of disease prevalence",
       fill = "Credible interval") +
  #ylim(0, 0.30) + 
  xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
#theme(legend.position = "bottom")
```

### Figure 3E
```{r, fig.height=12, fig.width=15}
# zi
wssta_zi <- ggplot(res_draws, aes(x = rWSSTA, y = zi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Blues") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Proportion of 0 disease prevalence",
       fill = "Credible interval") +
  #ylim(0, 0.30) + 
  xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
#theme(legend.position = "bottom")
```

### Figure 3F
```{r, fig.height=12, fig.width=15}
# phi
wssta_phi <- ggplot(res_draws, aes(x = rWSSTA, y = phi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greens") +
  labs(x =  "WSSTA (\u00B0C-weeks)", y = "Precison (1/SE) of disease prevalence",
       fill = "Credible interval") +
  xlim(0, 3.5) +
  theme_bw(base_size = 12) +
  theme(legend.position = "none")
```

### Figure 3G
```{r, fig.height=12, fig.width=15}

# year

# new data range
sYear_seq <- seq(min(dat$sYear),max(dat$sYear),length.out = 100)

# creating new data
new_year<- expand_grid(
  sYear = sYear_seq,
  sSumTemp = 0,
  sWSSTA = 0,
  sDisease_Num = 0,
  Ocean = c("Atlantic Ocean","Pacific Ocean","Indian Ocean") # this is for Atlantic
)

res_draws <- no_interaction %>% 
  epred_draws(newdata = new_year, dpar = c("mu", "zi", "phi"), re_formula = NA) %>% 
  group_by(sYear, .draw) %>% summarise(sYear = mean(sYear),
                                       rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean),
                                       predicted = mean(.epred),
                                       mu = mean(mu),
                                       zi = mean(zi),
                                       phi = mean(phi)) 

# mu
year_mu <- ggplot(res_draws, aes(x = rYear, y = mu)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Reds") +
  labs(x =  "Year", y = "Proportion of disease prevalence",
       fill = "Credible interval for mu") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

```

### Figure 3H
```{r, fig.height=12, fig.width=15}


# zi
year_zi <- ggplot(res_draws, aes(x = rYear, y = zi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Blues") +
  labs(x =  "Year", y = "Proportion of 0 disease prevalence",
       fill = "Credible interval for zi") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")
```

### Figure 3I
```{r, fig.height=12, fig.width=15}

# phi
year_phi <- ggplot(res_draws, aes(x = rYear, y = phi)) +
  stat_lineribbon() + 
  scale_fill_brewer(palette = "Greens") +
  labs(x =  "Year", y = "Precison (1/SE) of disease prevalence",
       fill = "Credible interval for phi") +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom")

```

### Combine plots {.active}
```{r, fig.height=12, fig.width=15}

(temp_mu + temp_zi + temp_phi)   / 
  (wssta_mu  + wssta_zi  + wssta_phi)   / 
  (year_mu + year_zi + year_phi)  + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))
```
## {-}

## Figure 4 {.tabset}
Three oceans’ predicted non-zero values (mu) of disease prevalence per fixed variable

### Getting data ready
```{r}
dat <-YearxOcean_SSTxOcean$data
```

### Figure 4A
```{r, fig.height=12, fig.width=15}

# summer temp

# create new data
means_fixed <- YearxOcean_SSTxOcean %>% 
  emmeans(~ sSumTemp + Ocean,
          at = list(sSumTemp = seq(min(dat$sSumTemp),max(dat$sSumTemp),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>%  
  gather_emmeans_draws() %>%
  mutate(rSumTemp = (sSumTemp*meanSD$SumTemp_SD + meanSD$SumTemp_mean))

# separate data by ocean and plot
means_fixed_A <- subset(means_fixed, Ocean == "Atlantic Ocean")
means_fixed_I <- subset(means_fixed, Ocean == "Indian Ocean")
means_fixed_P <- subset(means_fixed, Ocean == "Pacific Ocean")

# Atlantic Ocean
temp_A <- ggplot(means_fixed_A, aes(x = rSumTemp, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  #facet_wrap(vars(Ocean), ncol = 3) +
  labs(x = "Average Summer SST (\u00B0C)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "none", text = element_text(size = 12))
```

### Figure 4B
```{r, fig.height=12, fig.width=15}

# Indian Ocean
temp_I <- ggplot(means_fixed_I, aes(x = rSumTemp, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.45) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Average Summer SST (\u00B0C)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "none", text = element_text(size = 12))

```

### Figure 4C
```{r, fig.height=12, fig.width=15}

# Pacific Ocean
temp_P <- ggplot(means_fixed_P, aes(x = rSumTemp, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.9) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Average Summer SST (\u00B0C)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "none", text = element_text(size = 12))

```

### Figure 4D
```{r, fig.height=12, fig.width=15}

#wssta

# create new data
means_fixed <- YearxOcean_SSTxOcean %>% 
  emmeans(~ sWSSTA + Ocean,
          at = list(sWSSTA = seq(min(dat$sWSSTA),max(dat$sWSSTA),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>%  
  gather_emmeans_draws() %>%
  mutate(rWSSTA = (sWSSTA*meanSD$WSSTA_SD + meanSD$WSSTA_mean))

# separate data by ocean and plot
means_fixed_A <- subset(means_fixed, Ocean == "Atlantic Ocean")
means_fixed_I <- subset(means_fixed, Ocean == "Indian Ocean")
means_fixed_P <- subset(means_fixed, Ocean == "Pacific Ocean")

# Atlantic Ocean
wssta_A <- ggplot(means_fixed_A, aes(x = rWSSTA, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "WSSTA (\u00B0C-weeks)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.2) +
  xlim(0,3.5) +
  theme(legend.position = "none", text = element_text(size = 12))
```

### Figure 4E
```{r, fig.height=12, fig.width=15}

# Indian Ocean
wssta_I <- ggplot(means_fixed_I, aes(x = rWSSTA, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.45) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "WSSTA (\u00B0C-weeks)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.2) +
  xlim(0,3.5) +
  theme(legend.position = "none", text = element_text(size = 12))
```

### Figure 4F
```{r, fig.height=12, fig.width=15}

# Pacific Ocean
wssta_P <- ggplot(means_fixed_P, aes(x = rWSSTA, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.9) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "WSSTA (\u00B0C-weeks)",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.2) +
  xlim(0,3.5) +
  theme(legend.position = "none", text = element_text(size = 12))
```

### Figure 4G
```{r, fig.height=12, fig.width=15}

# year

# create new data
means_fixed <- YearxOcean_SSTxOcean %>% 
  emmeans(~ sYear + Ocean,
          at = list(sYear = seq(min(dat$sYear),max(dat$sYear),length.out = 100)),
          epred = TRUE,
          re_formula = NA) %>%  
  gather_emmeans_draws() %>%
  mutate(rYear = (sYear*meanSD$Year_SD + meanSD$Year_mean))

# separate data by ocean and plot
means_fixed_A <- subset(means_fixed, Ocean == "Atlantic Ocean")
means_fixed_I <- subset(means_fixed, Ocean == "Indian Ocean")
means_fixed_P <- subset(means_fixed, Ocean == "Pacific Ocean")

# Atlantic Ocean
year_A <- ggplot(means_fixed_A,
                 aes(x = rYear, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Year",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "bottom", text = element_text(size = 12))
```

### Figure 4H
```{r, fig.height=12, fig.width=15}

# Indian Ocean
year_I <- ggplot(means_fixed_I,
                 aes(x = rYear, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.45) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Year",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "bottom", text = element_text(size = 12))

```

### Figure 4I
```{r, fig.height=12, fig.width=15}

# Pacific Ocean
year_P <- ggplot(means_fixed_P,
                 aes(x = rYear, y = (.value), color = Ocean, fill = Ocean)) +
  stat_lineribbon(aes(fill_ramp = stat(level))) +
  scale_fill_viridis_d(begin = 0.9) +
  scale_colour_viridis_d(option = "B", end = 0) + 
  scale_fill_ramp_discrete(range = c(0.2, 0.7)) +
  labs(x = "Year",
       y = "Proportion of disease prevalence",
       fill_ramp = "Credible interval") +
  theme_bw(base_size = 14) +
  ylim(0, 0.35) +
  theme(legend.position = "bottom", text = element_text(size = 12))
```

### Combine plots {.active}
```{r, fig.height=12, fig.width=15}

(temp_A + temp_I + temp_P) / 
  (wssta_A  + wssta_I  + wssta_P) / 
  (year_A + year_I + year_P) + plot_annotation(tag_levels = 'A') + 
  plot_layout(guides = "collect") & 
  theme(legend.position='bottom', 
        text = element_text(size = 12))
```
## {-}

## Figure S4
Correlation between Average Summer SST and Year
```{r}
ggplot(rdsdat) +
  geom_point(aes(y = average_SST_summer, x = Year)) +
  labs(y = "Average Summer SST (\u00B0C)") +
  theme_bw()
```


## Figure S5
Visual description of WSSTA calculation
```{r, eval = F}
# define an updated comparison operator that will work well with values differing beyond the floating point precision limit

`%===%` <- function(x, y, tol = 1e-7) {
  
  if(length(x) == 1) {
    a = x; b = y
  } else {
    a = y; b = x
  }
  
  if(length(a) == 1 & length(b) == 1) {
    testout <- isTRUE(all.equal(x, y, tolerance = tol))
  }
  
  if(length(a) == 1 | length(b) == 1) {
    testout <- sapply(b, function(n, num) isTRUE(all.equal(n, num, tolerance = tol)), num = a)
  }
  
  if(length(a) > 1 & length(b) > 1) {
    testout <- mapply(function(n, m) isTRUE(all.equal(n, m, tolerance = tol)), a, b)
    
    if(length(a) != length(b)) warning("Objects differ in length, recycling the shorter object!")
  }
  
  return(testout)
}

# tests of the function
1 %===% 1
c(1,2,3) %===% 2
c(1,2,3) %===% c(3,2,4)
2 %===% c(3,2,4)
2.0005 %===% c(3,2,4)
2.00000006 %===% c(3,2.00000003,4)
2 %===% c(3,2.00000003,4)
c(1,2,3,4) %===% c(1,3)

# open a connection to a sample file to see its attributes
# (commented as it's usable only with all individual files available)
# nc_temp <- nc_open('./2015/20150101120000-ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_CDR2.1-v02.0-fv01.0.nc')
# print(nc_temp)

# example of extracting only some of a specific variable
# varpart <- ncvar_get(nc_temp, "analysed_sst", start = c(1,50,1), count = c(-1,10,1))
# is equivalent to this
# varpart <- ncvar_get(nc_temp, "analysed_sst", start = c(1,50), count = c(-1,10))
# dimensions are provided as X-Y-(Z)-T, and they are lon-lat-time

# extract the allowed values of dimensions
# (commented for reasons explained above)
# lonvar <- ncvar_get(nc_temp, "lon")
# latvar <- ncvar_get(nc_temp, "lat")

# reload limited data (defined largely in several commented lines)
# note: full NC file data are not included in repo due to their size
load(here('R', 'dhw_illustration_plot', 'required.Rdata'))

# load climatology
mmm <- nc_open(here("data",'ct5km_climatology_v3.1.nc'))
lons <- round(ncvar_get(mmm, 'lon'), 3)
lats <- round(ncvar_get(mmm, 'lat'), 3)

# check coordinates
lon_index <- which(lonvar %===% round(floor(114.6548/0.05)*0.05 + 0.025, 3))
lat_index <- which(latvar %===% round(floor(-8.14028/0.05)*0.05 + 0.025, 3))
mmmlon <- which(lons %===% round(floor(lonvar[lon_index]/0.05)*0.05 + 0.025, 3))
mmmlat <- which(lats %===% round(floor(latvar[lat_index]/0.05)*0.05 + 0.025, 3))

# (commented for reasons explained above)
# sst_ts <- c()
# for(i in list.files("./2017/")) {
#   nc_temp <- nc_open(paste0("./2017/", i))
#   sst_data <- ncvar_get(nc_temp, "analysed_sst",
#                         start = c(lon_index, lat_index, 1), count = c(1,1,1))
#   sst_ts <- c(sst_ts, sst_data)
#   nc_close(nc_temp)
# }
# repeat above code to load separate sst data for 2015, 2016, 2017
# sst_ts -> sst_ts_2015
# sst_ts -> sst_ts_2016
# sst_ts -> sst_ts_2017

# below code saves .Rdata file that is later used to reload yearly data
# limited to specific coordinates
# save(list = c('sst_ts_2015', 'sst_ts_2016', 'sst_ts_2017', 'lonvar', 'latvar'),
#      file = here('R', 'dhw_illustration_plot', 'required.Rdata'))


# extract location's climatology
mmm_climatology <- c(
  ncvar_get(mmm, "sst_clim_january", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_february", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_march", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_april", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_may", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_june", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_july", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_august", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_september", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_october", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_november", start = c(mmmlon, mmmlat, 1), count = c(1,1,1)),
  ncvar_get(mmm, "sst_clim_december", start = c(mmmlon, mmmlat, 1), count = c(1,1,1))
)
mmm_climatology

sst_ts <- sst_ts_2017 # change this to change year (data are: sst_ts_2015/2016/2017)
data <- data.frame(day = 1:length(sst_ts), sst = sst_ts-273)

# calculate month middles (floor-rounded)
# define months
months <- c(31,29,31,30,31,30,31,31,30,31,30,31)
months_cumul <- c(1, cumsum(months))
m_mids <- c()
for (i in 1:12) {
  mid <- floor((months_cumul[i] + months_cumul[i+1])/2)
  m_mids <- c(m_mids, mid)
}

weeks <- c(1, 1:52 * 7)
w_mids <- c()
for (i in 1:length(weeks)) {
  mid <- floor((weeks[i] + weeks[i+1])/2)
  if(!is.na(mid)) w_mids <- c(w_mids, mid)
}

i <- 1
sst_weekly <- c()
for(temp in data$sst) {
  
  if(i == 1) sst_avg <- temp*1/7
  else sst_avg <- sst_avg + temp*1/7
  
  if(i == 7) {sst_weekly <- c(sst_weekly, sst_avg); i <- 1}
  else i <- i+1
}

data.w <- data.frame(w_mids = w_mids, w_means = sst_weekly,
                     wssta = cumsum(ifelse(sst_weekly > max(mmm_climatology)+1,
                                         sst_weekly-max(mmm_climatology)+1, 0)))

# scale and fact are used to transform the second y axis
scale <- 25
fact <- 25
plot <- ggplot(data = data, mapping = aes(x = day)) +
  geom_hline(yintercept = max(mmm_climatology), lty = 5, lwd = 0.5, col = 'purple') +
  geom_hline(yintercept = max(mmm_climatology) + 1, lty = 5, lwd = 0.5, col = 'red') +
  scale_x_continuous(breaks = months_cumul) +
  scale_y_continuous(name = 'Sea Surface Temperature (\u00B0C)', limits = c(25, 31),
                     sec.axis = sec_axis(~(.-fact)*scale, name = "Cumulative Heat Stress (\u00B0C-weeks)")) +
  geom_line(aes(y = sst), col = 'gray80', lwd = 1.5) +
  geom_segment(data = subset(data.w, w_means > max(mmm_climatology) + 1),
               aes(x = w_mids, y = max(mmm_climatology) + 1, xend = w_mids, yend = w_means),
               lwd = 2, col = 'coral') +
  geom_point(data = data.frame(m_mids = m_mids, m_means = mmm_climatology),
             aes(x = m_mids, y = m_means), shape = 3, size = 5, col = 'red', stroke = 1) +
  geom_point(data = data.w,
             aes(x = w_mids, y = w_means), shape = 19, size = 2.5, col = 'gray40') +
  geom_ribbon(data = data.w, aes(x = w_mids, ymax = (wssta/scale)+fact), ymin = fact, fill = 'red', alpha = 0.1) +
  geom_line(data = data.w, aes(x = w_mids, y = (wssta/scale)+fact), col = 'coral', lwd = 3) +
  geom_point(x = max(data.w$w_mids), y = max((data.w$wssta/scale)+fact), shape = 19, size = 6, col = 'coral') +
  theme_bw() +
  theme(panel.grid.minor.x = element_blank(), text = element_text(size = 20)) +
  labs(x = 'Days')
plot
```


## Figure S7
Phylogenetic Tree of included species
```{r}
genus <- unique(prevSPP$Genus[prevSPP$Genus != "Helioseris"]) # Removed the Genus Helioseris because it was removed from the analysed dataset because of the disease prevalence metric used
taxa_list <- na.omit(genus)
taxa_list2 <- as.factor(taxa_list)
taxa <- tnrs_match_names(names = levels(taxa_list2), context_name = "Animals") # Connect the list of Genera with the Open Tree taxonomy

taxa.in.tree <- ott_id(taxa)[is_in_tree(ott_id(taxa))] 

tree <- tol_induced_subtree(ott_ids = taxa.in.tree, label_format = "name")
tree$tip.label <- gsub("_\\(.*","", tree$tip.label) # Remove symbols

plain_tree <- plot(tree, cex = 0.5, label.offset = 0.1, no.margin = TRUE) 

is.binary(tree) # Check if binary
tree <- compute.brlen(tree, method = "Grafen", power = 1) # Compute branch lengths 

all_taxa <- intersect(as.character(tree$tip.label), taxa_list) # Find synonyms and typos and remove them
used_taxa <- setdiff(as.character(tree$tip.label), taxa_list)

# Build heatmap of oceans
species <- select(prevSPP, "PaperID", "Genus")
species$Paper_ID <- species$PaperID
dat.tree <- left_join(rdsdat, species, by = c("Paper_ID")) # Join the data with the Genus information
dat.tree <- select(dat.tree, Paper_ID, Genus, Ocean) # Reduce the data to the needed columns
dat.tree <- mutate(dat.tree, search_string = decapitalize(Genus)) # Decapitalize "Genus" to match the "search string" in the "taxa" file
dat.tree <- left_join(dat.tree, select(taxa, search_string, unique_name, ott_id), by = "search_string") # Join taxa information from rotl to the dataset

dat.tree$unique_name <- word(dat.tree$unique_name, 1) # Only keep one word for the Genus (we had e.g. "Stylophora (genus in phylum Cnidaria)")

dat.tree <- dat.tree[dat.tree$unique_name %in% tree$tip.label, ] # Check that the Genera match the ones used in the tree

ocean <- dat.tree %>% group_by(unique_name) %>% summarise( 
  oceanIndian = Ocean == "Indian Ocean", 
  oceanAtlantic = Ocean == "Atlantic Ocean",
  oceanPacific = Ocean == "Pacific Ocean")
ocean <- distinct(ocean) # Only keep unique rows
ocean$oceanIndian = as.numeric(ocean$oceanIndian) # convert TRUE/FALSE to binary values for each Ocean
ocean$oceanAtlantic = as.numeric(ocean$oceanAtlantic)
ocean$oceanPacific = as.numeric(ocean$oceanPacific)

ocean <- ocean %>% group_by(unique_name) %>% summarise(oceanIndian=sum(oceanIndian), # calculate the sum for each species (i.e., if 1, the species is found in the designated ocean)
                                                     oceanAtlantic=sum(oceanAtlantic),
                                                     oceanPacific=sum(oceanPacific))
ocean$oceanIndian=as.factor(ocean$oceanIndian) # Convert back to factor for the plot
ocean$oceanAtlantic=as.factor(ocean$oceanAtlantic)
ocean$oceanPacific=as.factor(ocean$oceanPacific)

# Plot Horizontal Tree
# simple phylogenetic tree
htree <- ggtree(tree, lwd = 1.05) + 
  geom_tiplab(offset = 0.04) # Display Genus

h <- htree %<+% ocean # Link plot to data

# plot tree and heatmap together
h2 <- h +  geom_fruit(geom=geom_tile, 
                     mapping=aes(fill=oceanAtlantic), 
                     width=0.075, 
                     offset=0.35) + 
  scale_fill_manual(name = "", values=c("white", "#7C4F85"), labels=c("", "Atlantic Ocean"), guide = guide_legend(order = 1)) +
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanIndian), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white","#71A8AF"), labels=c("", "Indian Ocean"), guide = guide_legend(order = 2))+
  new_scale_fill() + 
  geom_fruit(geom=geom_tile, 
             mapping=aes(fill=oceanPacific), 
             width=0.075, 
             offset=0.075)+
  scale_fill_manual(name = "", values=c("white", "#D4E974"), labels=c("", "Pacific Ocean"), guide = guide_legend(order = 3))

h2
```


## Figure S8 {.tabset}
No interaction contrasts

### Average Summer SST
```{r, fig.height=12, fig.width=15}
# create new data
trends_draws2 <- no_interaction %>% 
  emtrends(~ sSumTemp, var = "sSumTemp",
           at = list(sSumTemp = c((25-meanSD$SumTemp_mean)/meanSD$SumTemp_SD, (32-meanSD$SumTemp_mean)/meanSD$SumTemp_SD)), # 25 and 32 degree
           regrid = "response") %>% 
  contrast(method = "revpairwise") %>% 
  gather_emmeans_draws()

# plot contrast
temp_cont <- ggplot(trends_draws2, aes(x = .value)) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               fill = "grey65") +
  labs(x = "Difference in marginal effects between 25\u00B0C and 32\u00B0C", y = NULL) +
  theme_bw() 
```

### WSSTA
```{r, fig.height=12, fig.width=15}
# create new data
trends_draws2 <- no_interaction %>% 
  emtrends(~ sWSSTA, var = "sWSSTA",
           at = list(sWSSTA = c((0.4-meanSD$WSSTA_mean)/meanSD$WSSTA_SD, (4.3-meanSD$WSSTA_mean)/meanSD$WSSTA_SD)), 
           regrid = "response") %>% 
  contrast(method = "revpairwise") %>% 
  gather_emmeans_draws()

wssta_cont <- ggplot(trends_draws2, aes(x = .value)) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               fill = "grey65") +
  labs(x = "Difference in marginal effects between 0.4\u00B0C-weeks and 4.3\u00B0C-weeks", y = NULL) +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = "dotted")
```

### Year 
```{r, fig.height=12, fig.width=15}
# create new data
trends_draws2 <- no_interaction %>% 
  emtrends(~ sYear, var = "sYear",
           at = list(sYear = c((1992-meanSD$Year_mean)/meanSD$Year_SD, (2018-meanSD$Year_mean)/meanSD$Year_SD)),
           regrid = "response") %>% 
  contrast(method = "revpairwise") %>% 
  gather_emmeans_draws()

# plot contrast
year_cont <- ggplot(trends_draws2, aes(x = .value)) +
  stat_halfeye(.width = c(0.8, 0.95), point_interval = "median_hdi",
               fill = "grey65") +
  labs(x = "Difference in marginal effects between 1988 and 2018", y = NULL,
       caption = "80% and 95% credible intervals shown in black") +
  theme_bw() +
  geom_vline(xintercept = 0, linetype = "dotted")
```

### Contrast figure {.active}
```{r, fig.height=12, fig.width=15}
temp_cont + wssta_cont + year_cont + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))
```
## {-}

## Figure S9 {.tabset}
Ocean contrast plots

### Average Summer SST
```{r, fig.height=12, fig.width=15}
# create new data
trends_draws <- YearxOcean_SSTxOcean %>% 
  emtrends(~  Ocean, var = "sSumTemp",
           #at = list(sSumTemp = c(0)),
           regrid = "none") %>% 
  contrast(method = "pairwise") %>% 
  gather_emmeans_draws()

# plot contrast
cont_temp <- ggplot(trends_draws,
                    aes(x = .value, fill = factor(contrast))) +
  stat_halfeye(slab_alpha = 0.75) +
  scale_fill_okabe_ito(order = c(3, 4, 5)) +
  facet_wrap(vars(contrast)) +
  theme_bw() +
  labs(x = "Difference in marginal effects between 25\u00B0C and 32\u00B0C", y = NULL)  +
  theme(legend.position = "none")
```

### WSSTA
No WSSTA since there was no interaction with Ocean

### Year
```{r, fig.height=12, fig.width=15}
# create new data
trends_draws <- YearxOcean_SSTxOcean %>% 
  emtrends(~  Ocean, var = "sYear",
           #at = list(sSumTemp = c(0)),
           regrid = "none") %>% 
  contrast(method = "pairwise") %>% 
  gather_emmeans_draws()

# plot contrast
cont_year<- ggplot(trends_draws,
                   aes(x = .value, fill = factor(contrast))) +
  stat_halfeye(slab_alpha = 0.75) +
  scale_fill_okabe_ito(order = c(3, 4, 5)) +
  facet_wrap(vars(contrast)) +
  theme_bw() +
  labs(x = "Difference in marginal effects between 1992 and 2018", y = NULL,
       caption = "80% and 95% credible intervals shown in black") +
  theme(legend.position = "bottom", legend.title=element_blank())
```

### Contrast figure {.active}
```{r, fig.height=12, fig.width=15}
cont_temp / cont_year + plot_annotation(tag_levels = 'A') & theme(text = element_text(size = 12))

```
## {-}

# **Software and Package Versions**

```{r}
sessionInfo()
```

